Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • English  (366)
  • Birmingham, UK : Packt Publishing  (366)
  • Cambridge : Cambridge University Press
  • Python (Computer program language)  (262)
  • Machine learning  (169)
Datasource
Material
Language
  • English  (366)
Years
Author, Corporation
Publisher
Keywords
Subjects(RVK)
  • 1
    Online Resource
    Online Resource
    MATLAB for machine learning (2024)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781835087695
    Language: English
    Pages: 1 online resource (374 pages) , illustrations
    Edition: Second edition.
    DDC: 518.0285/536
    Keywords: MATLAB ; Machine learning ; Computer programming
    Abstract: Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applications Key Features Work with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithms Evaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoring Uncover effective approaches to deep learning for computer vision, time series analysis, and forecasting Purchase of the print or Kindle book includes a free PDF eBook Book Description Discover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you'll learn how to seamlessly interact with the workspace. You'll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you'll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You'll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you'll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you'll be able to put it all together by applying major machine learning algorithms in real-world scenarios. What you will learn Discover different ways to transform data into valuable insights Explore the different types of regression techniques Grasp the basics of classification through Naive Bayes and decision trees Use clustering to group data based on similarity measures Perform data fitting, pattern recognition, and cluster analysis Implement feature selection and extraction for dimensionality reduction Harness MATLAB tools for deep learning exploration Who this book is for This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.
    Note: Includes index
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 2
    Online Resource
    Online Resource
    Data labeling in machine learning with Python : modern ways to prepare the labeled data for training and fine-tuning ML and AI models (2023)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781804613788 , 1804613789 , 9781804610541
    Language: English
    Pages: 1 online resource (398 pages) , illustrations
    Parallel Title: Erscheint auch als
    DDC: 005.13/3
    Keywords: Python (Computer program language) ; Machine learning ; Computer programming ; Python (Langage de programmation) ; Apprentissage automatique ; Programmation (Informatique) ; computer programming
    Abstract: Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labeling Key Features Generate labels for regression in scenarios with limited training data Apply generative AI and large language models (LLMs) to explore and label text data Leverage Python libraries for image, video, and audio data analysis and data labeling Purchase of the print or Kindle book includes a free PDF eBook Book Description Data labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today's data-driven world, mastering data labeling is not just an advantage, it's a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you'll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data. What you will learn Excel in exploratory data analysis (EDA) for tabular, text, audio, video, and image data Understand how to use Python libraries to apply rules to label raw data Discover data augmentation techniques for adding classification labels Leverage K-means clustering to classify unsupervised data Explore how hybrid supervised learning is applied to add labels for classification Master text data classification with generative AI Detect objects and classify images with OpenCV and YOLO Uncover a range of techniques and resources for data annotation Who this book is for This book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.
    Note: Includes index
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 3
    Online Resource
    Online Resource
    Data science for marketing analytics (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789952100 , 1789952107
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Marketing ; Data processing ; Marketing research ; Python (Computer program language) ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features Study new techniques for marketing analytics Explore uses of machine learning to power your marketing analyses Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learn Analyze and visualize data in Python using pandas and Matplotlib Study clustering techniques, such as hierarchical and k-means clustering Create customer segments based on manipulated data Predict customer lifetime value using linear regression Use classification algorithms to understand customer choice Optimize classification algorithms to extract maximal information Who this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.
    Note: Includes bibliographical references. - Description based on online resource; title from copyright page (Safari, viewed May 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789959413/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Applied unsupervised learning with R (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789951462 , 1789951461
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Machine learning ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data. Key Features Build state-of-the-art algorithms that can solve your business' problems Learn how to find hidden patterns in your data Revise key concepts with hands-on exercises using real-world datasets Book Description Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection. What you will learn Implement clustering methods such as k-means, agglomerative, and divisive Write code in R to analyze market segmentation and consumer behavior Estimate distribution and probabilities of different outcomes Implement dimension reduction using principal component analysis Apply anomaly detection methods to identify fraud Design algorithms with R and learn how to edit or improve code Who this book is for Applied Unsupervised Learning with R is designed for business professionals who want to learn about methods to understand their data better, and developers who have an interest in unsupervised learning. Although the book is for beginners, it will be beneficial to have some basic, beginner-level familiarity with R. This includes an understanding of how to open the R console, how to read data, and how to create a loop. To easily understand the concepts of this book, you should also know basic mathematical concepts, including ex...
    Note: Includes bibliographical references. - Description based on online resource; title from copyright page (Safari, viewed May 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789956399/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 5
    Online Resource
    Online Resource
    Foundations of Blockchain : the pathway to cryptocurrencies and decentralized blockchain applications (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789138191 , 1789138191
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Blockchains (Databases) ; Application software ; Development ; Cryptocurrencies ; Bitcoin ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Learn the foundations of blockchain technology - its core concepts and algorithmic solutions across cryptography, peer-to-peer technology, and game theory. Key Features Learn the core concepts and foundations of the blockchain and cryptocurrencies Understand the protocols and algorithms behind decentralized applications Master how to architect, build, and optimize blockchain applications Book Description Blockchain technology is a combination of three popular concepts: cryptography, peer-to-peer networking, and game theory. This book is for anyone who wants to dive into blockchain from first principles and learn how decentralized applications and cryptocurrencies really work. This book begins with an overview of blockchain technology, including key definitions, its purposes and characteristics, so you can assess the full potential of blockchain. All essential aspects of cryptography are then presented, as the backbone of blockchain. For readers who want to study the underlying algorithms of blockchain, you'll see Python implementations throughout. You'll then learn how blockchain architecture can create decentralized applications. You'll see how blockchain achieves decentralization through peer-to-peer networking, and how a simple blockchain can be built in a P2P network. You'll learn how these elements can implement a cryptocurrency such as Bitcoin, and the wider applications of blockchain work through smart contracts. Blockchain optimization techniques, and blockchain security strategies are then presented. To complete this foundation, we consider blockchain applications in the financial and non-financial sectors, and also analyze the future of blockchain. A study of blockchain use cases includes supply chains, payment systems, crowdfunding, and DAOs, which rounds out your foundation in blockchain technology. What you will learn The core concepts and technical foundations of blockchain The algorithmic principles and solutions that make up blockchain and cryptocurrencies Blockchain cryptography explained in detail How to realize blockchain projects with hands-on Python code How to architect the blockchain and blockchain applications Decentralized application development with MultiChain, NEO, and Ethereum Optimizing and enhancing blockchain performance and security Classical blockchain use cases and how to implement them Who this book is for This book is for anyone who wants to dive into blockchain technology from first principles and build a ...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 14, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789139396/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 6
    Online Resource
    Online Resource
    Generative adversarial networks projects : build next-generation generative models using TensorFlow and Keras (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789134193 , 1789134196
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Explore various Generative Adversarial Network architectures using the Python ecosystem Key Features Use different datasets to build advanced projects in the Generative Adversarial Network domain Implement projects ranging from generating 3D shapes to a face aging application Explore the power of GANs to contribute in open source research and projects Book Description Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain. Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you'll gain an understanding of the architecture and functioning of generative models through their practical implementation. By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects. What you will learn Train a network on the 3D ShapeNet dataset to generate realistic shapes Generate anime characters using the Keras implementation of DCGAN Implement an SRGAN network to generate high-resolution images Train Age-cGAN on Wiki-Cropped images to improve face verification Use Conditional GANs for image-to-image translation Understand the generator and discriminator implementations of StackGAN in Keras Who this book is for If you're a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789136678/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 7
    Online Resource
    Online Resource
    Hands-on penetration testing with Python : enhance your ethical hacking skills to build automated and intelligent systems (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788999465 , 1788999460
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Penetration testing (Computer security) ; Electronic books ; Electronic books ; local
    Abstract: Implement defensive techniques in your ecosystem successfully with Python Key Features Identify and expose vulnerabilities in your infrastructure with Python Learn custom exploit development . Make robust and powerful cybersecurity tools with Python Book Description With the current technological and infrastructural shift, penetration testing is no longer a process-oriented activity. Modern-day penetration testing demands lots of automation and innovation; the only language that dominates all its peers is Python. Given the huge number of tools written in Python, and its popularity in the penetration testing space, this language has always been the first choice for penetration testers. Hands-On Penetration Testing with Python walks you through advanced Python programming constructs. Once you are familiar with the core concepts, you'll explore the advanced uses of Python in the domain of penetration testing and optimization. You'll then move on to understanding how Python, data science, and the cybersecurity ecosystem communicate with one another. In the concluding chapters, you'll study exploit development, reverse engineering, and cybersecurity use cases that can be automated with Python. By the end of this book, you'll have acquired adequate skills to leverage Python as a helpful tool to pentest and secure infrastructure, while also creating your own custom exploits. What you will learn Get to grips with Custom vulnerability scanner development Familiarize yourself with web application scanning automation and exploit development Walk through day-to-day cybersecurity scenarios that can be automated with Python Discover enterprise-or organization-specific use cases and threat-hunting automation Understand reverse engineering, fuzzing, buffer overflows , key-logger development, and exploit development for buffer overflows. Understand web scraping in Python and use it for processing web responses Explore Security Operations Centre (SOC) use cases Get to understand Data Science, Python, and cybersecurity all under one hood Who this book is for If you are a security consultant , developer or a cyber security enthusiast with little or no knowledge of Python and want in-depth insight into how the pen-testing ecosystem and python combine to create offensive tools , exploits , automate cyber security use-cases and much more then this book is for you. Hands-On Penetration Testing with Python guides you through the advanced uses of Python for cybersecu...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788990820/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 8
    Online Resource
    Online Resource
    Hands-on deep learning for games : leverage the power of neural networks and reinforcement learning to build intelligent games (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788998765 , 1788998766
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Reinforcement learning ; Machine learning ; Computer games ; Programming ; Neural networks (Computer science) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key Features Apply the power of deep learning to complex reasoning tasks by building a Game AI Exploit the most recent developments in machine learning and AI for building smart games Implement deep learning models and neural networks with Python Book Description The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron's to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning. What you will learn Learn the foundations of neural networks and deep learning. Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots. Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems. Working with Unity ML-Agents toolkit and how to install, setup and run the kit. Understand core concepts of DRL and the differences between discrete and continuous action environments. Use several advanced forms of learning in various scenarios from developing agents to testing games. Who this book is for This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concept...
    Note: Description based on online resource; title from title page (Safari, viewed May 14, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788994071/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 9
    Online Resource
    Online Resource
    Hands-on artificial intelligence for IoT : expert machine learning and deep learning techniques for developing smarter IoT systems (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788832762 , 1788832760
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Internet of things ; Artificial intelligence ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Build smarter systems by combining artificial intelligence and the Internet of Things - two of the most talked about topics today Key Features Leverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT data Process IoT data and predict outcomes in real time to build smart IoT models Cover practical case studies on industrial IoT, smart cities, and home automation Book Description There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learn Apply different AI techniques including machine learning and deep learning using TensorFlow and Keras Access and process data from various distributed sources Perform supervised and unsupervised machine learning for IoT data Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms Forecast time-series data using deep learning methods Implementing AI from case studies in Personal IoT, Industrial IoT, and Smart Cities Gain unique insights from data obtained from wearable devices and smart devices Who this book is for If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how po...
    Note: Description based on online resource; title from title page (Safari, viewed March 19, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 10
    Online Resource
    Online Resource
    Hands-on Blockchain for Python developers : gain blockchain programming skills to build decentralized applications using Python (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788624640 , 1788624645
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Blockchains (Databases) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Implement real-world decentralized applications using Python, Vyper, Populus, and Ethereum Key Features Stay up-to-date with everything you need to know about the blockchain ecosystem Implement smart contracts, wallets, and decentralized applications(DApps) using Python libraries Get deeper insights into storing content in a distributed storage platform Book Description Blockchain is seen as the main technological solution that works as a public ledger for all cryptocurrency transactions. This book serves as a practical guide to developing a full-fledged decentralized application with Python to interact with the various building blocks of blockchain applications. Hands-On Blockchain for Python Developers starts by demonstrating how blockchain technology and cryptocurrency hashing works. You will understand the fundamentals and benefits of smart contracts such as censorship resistance and transaction accuracy. As you steadily progress, you'll go on to build smart contracts using Vyper, which has a similar syntax to Python. This experience will further help you unravel the other benefits of smart contracts, including reliable storage and backup, and efficiency. You'll also use web3.py to interact with smart contracts and leverage the power of both the web3.py and Populus framework to build decentralized applications that offer security and seamless integration with cryptocurrencies. As you explore later chapters, you'll learn how to create your own token on top of Ethereum and build a cryptocurrency wallet graphical user interface (GUI) that can handle Ethereum and Ethereum Request for Comments (ERC-20) tokens using the PySide2 library. This will enable users to seamlessly store, send, and receive digital money. Toward the end, you'll implement InterPlanetary File System (IPFS) technology in your decentralized application to provide a peer-to-peer filesystem that can store and expose media. By the end of this book, you'll be well-versed in blockchain programming and be able to build end-to-end decentralized applications on a range of domains using Python. What you will learn Understand blockchain technology and what makes it an immutable database Use the features of web3.py API to interact with the smart contract Create your own cryptocurrency and token in Ethereum using Vyper Use IPFS features to store content on the decentralized storage platform Implement a Twitter-like decentralized application with a desktop frontend Build decentralized app...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 28, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788627856/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 11
    Online Resource
    Online Resource
    Advanced Python programming : build high performance, concurrent, and multi-threaded apps with Python using proven design patterns (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781838553692 , 183855369X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Learning path
    Keywords: Python (Computer program language) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Create distributed applications with clever design patterns to solve complex problems Key Features Set up and run distributed algorithms on a cluster using Dask and PySpark Master skills to accurately implement concurrency in your code Gain practical experience of Python design patterns with real-world examples Book Description This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: ? Python High Performance - Second Edition by Gabriele Lanaro ? Mastering Concurrency in Python by Quan Nguyen ? Mastering Python Design Patterns by Sakis Kasampalis What you will learn Use NumPy and pandas to import and manipulate datasets Achieve native performance with Cython and Numba Write asynchronous code using asyncio and RxPy Design highly scalable programs with application scaffolding Explore abstract methods to maintain data consistency Clone objects using the prototype pattern Use the adapter pattern to make incompatible interfaces compatible Employ the strategy pattern to dynamically choose an algorithm Who this book is for This Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns. Some experience with Python programming language will help you get the most out of this Learning Path.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed April 22, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 12
    Online Resource
    Online Resource
    Building serverless microservices in Python : a complete guide to building, testing, and deploying microservices using serverless computing on AWS (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789533422 , 1789533422
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Application software ; Development ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: A practical guide for developing end-to-end serverless microservices in Python for developers, DevOps, and architects. Key Features Create a secure, cost-effective, and scalable serverless data API Use identity management and authentication for a user-specific and secure web application Go beyond traditional web hosting to explore the full range of cloud hosting options Book Description Over the last few years, there has been a massive shift from monolithic architecture to microservices, thanks to their small and independent deployments that allow increased flexibility and agile delivery. Traditionally, virtual machines and containers were the principal mediums for deploying microservices, but they involved a lot of operational effort, configuration, and maintenance. More recently, serverless computing has gained popularity due to its built-in autoscaling abilities, reduced operational costs, and increased productivity. Building Serverless Microservices in Python begins by introducing you to serverless microservice structures. You will then learn how to create your first serverless data API and test your microservice. Moving on, you'll delve into data management and work with serverless patterns. Finally, the book introduces you to the importance of securing microservices. By the end of the book, you will have gained the skills you need to combine microservices with serverless computing, making their deployment much easier thanks to the cloud provider managing the servers and capacity planning. What you will learn Discover what microservices offer above and beyond other architectures Create a serverless application with AWS Gain secure access to data and resources Run tests on your configuration and code Create a highly available serverless microservice data API Build, deploy, and run your serverless configuration and code Who this book is for If you are a developer with basic knowledge of Python and want to learn how to build, test, deploy, and secure microservices, then this book is for you. No prior knowledge of building microservices is required.
    Note: Description based on online resource; title from title page (Safari, viewed May 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789535297/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 13
    Online Resource
    Online Resource
    Neural networks with Keras cookbook : over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789342109 , 1789342104
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Neural networks (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Implement neural network architectures by building them from scratch for multiple real-world applications. Key Features From scratch, build multiple neural network architectures such as CNN, RNN, LSTM in Keras Discover tips and tricks for designing a robust neural network to solve real-world problems Graduate from understanding the working details of neural networks and master the art of fine-tuning them Book Description This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data. Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks. We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems. Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game. By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter. What you will learn Build multiple advanced neural network architectures from scratch Explore transfer learning to perform object detection and classification Build self-driving car applications using instance and semantic segmentation Understand data encoding for image, text and recommender systems Implement text analysis using sequence-to-sequence learning Leverage a combination of CNN and RNN to perform end-to-end learning Build agents to play games using deep Q-learning Who this book is for This intermediate-level book targets beginners and intermediate-level machine learning practitioners and data scientists who have just started their journey with neural networks. This book is for those who are looking for resources to help them navigate through the various neural network architectures; you'll build multiple architectures, with concomitant case studies ordered by the complexity of the problem. A basic un...
    Note: Description based on online resource; title from title page (Safari, viewed April 18, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789346640/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 14
    Online Resource
    Online Resource
    Getting started with Python for the Internet of Things : leverage the full potential of Python to prototype and build IoT projects using the Raspberry Pi (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781838553333 , 1838553339
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Learning path
    Keywords: Python (Computer program language) ; Internet of things ; Raspberry Pi (Computer) ; Machine-to-machine communications ; Electronic books ; Electronic books ; local
    Abstract: Build clever, collaborative, and powerful automation systems with the Raspberry Pi and Python. Key Features Create your own Pi-Rover or Pi-Hexipod robots Develop practical applications in Python using Raspberry Pi Build your own Jarvis, a highly advanced computerized AI Book Description This Learning Path takes you on a journey in the world of robotics and teaches you all that you can achieve with Raspberry Pi and Python. It teaches you to harness the power of Python with the Raspberry Pi 3 and the Raspberry Pi zero to build superlative automation systems that can transform your business. You will learn to create text classifiers, predict sentiment in words, and develop applications with the Tkinter library. Things will get more interesting when you build a human face detection and recognition system and a home automation system in Python, where different appliances are controlled using the Raspberry Pi. With such diverse robotics projects, you'll grasp the basics of robotics and its functions, and understand the integration of robotics with the IoT environment. By the end of this Learning Path, you will have covered everything from configuring a robotic controller, to creating a self-driven robotic vehicle using Python. Raspberry Pi 3 Cookbook for Python Programmers - Third Edition by Tim Cox, Dr. Steven Lawrence Fernandes Python Programming with Raspberry Pi by Sai Yamanoor, Srihari Yamanoor Python Robotics Projects by Prof. Diwakar Vaish What you will learn Build text classifiers and predict sentiment in words with the Tkinter library Develop human face detection and recognition systems Create a neural network module for optical character recognition Build a mobile robot using the Raspberry Pi as a controller Understand how to interface sensors, actuators, and LED displays work Apply machine learning techniques to your models Interface your robots with Bluetooth Who this book is for This Learning Path is specially designed for Python developers who want to take their skills to the next level by creating robots that can enhance people's lives. Familiarity with Python and electronics will aid understanding the concepts in this Learning Path.
    Note: Description based on online resource; title from title page (Safari, viewed April 5, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781838555795/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 15
    Online Resource
    Online Resource
    R machine learning projects : implement supervised, unsupervised, and reinforcement learning techniques using R 3.5 (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789806090 , 1789806097
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key Features Master machine learning, deep learning, and predictive modeling concepts in R 3.5 Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains Implement smart cognitive models with helpful tips and best practices Book Description R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you'll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You'll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations. What you will learn Explore deep neural networks and various frameworks that can be used in R Develop a joke recommendation engine to recommend jokes that match users' tastes Create powerful ML models with ensembles to predict employee attrition Build autoencoders for credit card fraud detection Work with image recognition and convolutional neural networks Make predictions for casino slot machine using reinforcement learning Implement NLP techniques for sentiment analysis and customer segmentation Who this book is for If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this book.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 6, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789807943/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 16
    Online Resource
    Online Resource
    Neural network projects with Python : the ultimate guide to using Python to explore the true power of neural networks through six projects (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789133318 , 1789133319
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Neural networks (Computer science) ; Machine learning ; Artificial intelligence ; Electronic books ; local ; Electronic books
    Abstract: Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key Features Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI Build expert neural networks in Python using popular libraries such as Keras Includes projects such as object detection, face identification, sentiment analysis, and more Book Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learn Learn various neural network architectures and its advancements in AI Master deep learning in Python by building and training neural network Master neural networks for regression and classification Discover convolutional neural networks for image recognition Learn sentiment analysis on textual data using Long Short-Term Memory Build and train a highly accurate facial recognition security system Who this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.
    Note: Description based on online resource; title from title page (Safari, viewed April 18, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 17
    Online Resource
    Online Resource
    Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789803198 , 1789803195
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Microsoft Cognitive Toolkit ; Neural networks (Computer science) ; Machine learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive Toolkit Key Features Understand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment Train different types of neural networks using Cognitive Toolkit and deploy it to production Evaluate the performance of your models and improve your deep learning skills Book Description Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks. This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment What you will learn Set up your deep learning environment for the Cognitive Toolkit on Windows and Linux Pre-process and feed your data into neural networks Use neural networks to make effcient predictions and recommendations Train and deploy effcient neural networks such as CNN and RNN Detect problems in your neural network using TensorBoard Integrate Cognitive Toolkit with Azure ML Services for effective deep learning Who this book is for Data Scientists, Machine learning developers, AI developers who wish to train and deploy effective deep learning models using Microsoft CNTK will find this book to be useful. Readers need to have experience in Python or similar object-oriented language like C# or Java.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789802993/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 18
    Online Resource
    Online Resource
    Hands-on neural networks with Keras : design and create neural networks using deep learning and artificial intelligence principles (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789533347 , 1789533341
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Your one-stop guide to learning and implementing artificial neural networks with Keras effectively Key Features Design and create neural network architectures on different domains using Keras Integrate neural network models in your applications using this highly practical guide Get ready for the future of neural networks through transfer learning and predicting multi network models Book Description Neural networks are used to solve a wide range of problems in different areas of AI and deep learning. Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks. By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization. What you will learn Understand the fundamental nature and workflow of predictive data modeling Explore how different types of visual and linguistic signals are processed by neural networks Dive into the mathematical and statistical ideas behind how networks learn from data Design and implement various neural networks such as CNNs, LSTMs, and GANs Use different architectures to tackle cognitive tasks and embed intelligence in systems Learn how to generate synthetic data and use augmentation strategies to improve your models Stay on top of the latest academic and commercial developments in the field of AI Who this book is for This book is for machine learning practitioners, deep learning researchers and AI enthusiasts who are looking to get well versed with different neural network architecture using Keras. Working knowledge of Python programming language is mandatory.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 14, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789536089/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 19
    Online Resource
    Online Resource
    Learning Python for forensics : leverage the power of Python in forensic investigations (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789342765 , 1789342767
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Computer networks ; Security measures ; Python (Computer program language) ; Computer crimes ; Investigation ; Data recovery (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Design, develop, and deploy innovative forensic solutions using Python Key Features Discover how to develop Python scripts for effective digital forensic analysis Master the skills of parsing complex data structures with Python libraries Solve forensic challenges through the development of practical Python scripts Book Description Digital forensics plays an integral role in solving complex cybercrimes and helping organizations make sense of cybersecurity incidents. This second edition of Learning Python for Forensics illustrates how Python can be used to support these digital investigations and permits the examiner to automate the parsing of forensic artifacts to spend more time examining actionable data. The second edition of Learning Python for Forensics will illustrate how to develop Python scripts using an iterative design. Further, it demonstrates how to leverage the various built-in and community-sourced forensics scripts and libraries available for Python today. This book will help strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials. By the end of this book, you will build a collection of Python scripts capable of investigating an array of forensic artifacts and master the skills of extracting metadata and parsing complex data structures into actionable reports. Most importantly, you will have developed a foundation upon which to build as you continue to learn Python and enhance your efficacy as an investigator. What you will learn Learn how to develop Python scripts to solve complex forensic problems Build scripts using an iterative design Design code to accommodate present and future hurdles Leverage built-in and community-sourced libraries Understand the best practices in forensic programming Learn how to transform raw data into customized reports and visualizations Create forensic frameworks to automate analysis of multiple forensic artifacts Conduct effective and efficient investigations through programmatic processing Who this book is for If you are a forensics student, hobbyist, or professional seeking to increase your understanding in forensics through the use of a programming language, then Learning Python for Forensics is for you. You are not required to have previous experience in programming to learn and master the content within this book. This material, created by forensic professionals, was written with a unique perspective and understanding for ex...
    Note: Previous edition published: 2016. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789341690/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 20
    Online Resource
    Online Resource
    Ensemble machine learning cookbook : over 35 practical recipes to explore ensemble machine learning techniques using Python (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789132502 , 1789132509
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Key Features Apply popular machine learning algorithms using a recipe-based approach Implement boosting, bagging, and stacking ensemble methods to improve machine learning models Discover real-world ensemble applications and encounter complex challenges in Kaggle competitions Book Description Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you'll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You'll also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes. What you will learn Understand how to use machine learning algorithms for regression and classification problems Implement ensemble techniques such as averaging, weighted averaging, and max-voting Get to grips with advanced ensemble methods, such as bootstrapping, bagging, and stacking Use Random Forest for tasks such as classification and regression Implement an ensemble of homogeneous and heterogeneous machine learning algorithms Learn and implement various boosting techniques, such as AdaBoost, Gradient Boosting Machine, and XGBoost Who this book is for This book is designed fo...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789136609/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 21
    Online Resource
    Online Resource
    Machine learning with the Elastic Stack : expert techniques to integrate machine learning with distributed search and analytics (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788471770 , 1788471776
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Information visualization ; Electronic data processing ; Distributed processing ; Open source software ; Electronic books ; Electronic books ; local
    Abstract: Leverage Elastic Stack's machine learning features to gain valuable insight from your data Key Features Combine machine learning with the analytic capabilities of Elastic Stack Analyze large volumes of search data and gain actionable insight from them Use external analytical tools with your Elastic Stack to improve its performance Book Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learn Install the Elastic Stack to use machine learning features Understand how Elastic machine learning is used to detect a variety of anomaly types Apply effective anomaly detection to IT operations and security analytics Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting Combine your created jobs to correlate anomalies of different layers of infrastructure Learn various tips and tricks to get the most out of Elastic machine learning Who this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.
    Note: Description based on online resource; title from title page (Safari, viewed March 22, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788477543/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 22
    Online Resource
    Online Resource
    Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789349276 , 1789349273
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Artificial intelligence ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Discover the skill-sets required to implement various approaches to Machine Learning with Python Key Features Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more Build your own neural network models using modern Python libraries Practical examples show you how to implement different machine learning and deep learning techniques Book Description Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges. What you will learn Use cluster algorithms to identify and optimize natural groups of data Explore advanced non-linear and hierarchical clustering in action Soft label assignments for fuzzy c-means and Gaussian mixture models Detect anomalies through density estimation Perform principal component analysis using neural network models Create unsupervised models using GANs Who this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed April 18, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789348279/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 23
    Online Resource
    Online Resource
    Mastering Python scripting for system administrators : write scripts and automate them for real-world administration tasks using Python (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789134261 , 1789134269
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Application software ; Development ; Information technology ; Management ; Electronic books ; Electronic books ; local
    Abstract: Leverage the features and libraries of Python to administrate your environment efficiently. Key Features Learn how to solve problems of system administrators and automate routine activities Learn to handle regular expressions, network administration Building GUI, web-scraping and database administration including data analytics Book Description Python has evolved over time and extended its features in relation to every possible IT operation. Python is simple to learn, yet has powerful libraries that can be used to build powerful Python scripts for solving real-world problems and automating administrators' routine activities. The objective of this book is to walk through a series of projects that will teach readers Python scripting with each project. This book will initially cover Python installation and quickly revise basic to advanced programming fundamentals. The book will then focus on the development process as a whole, from setup to planning to building different tools. It will include IT administrators' routine activities (text processing, regular expressions, file archiving, and encryption), network administration (socket programming, email handling, the remote controlling of devices using telnet/ssh, and protocols such as SNMP/DHCP), building graphical user interface, working with websites (Apache log file processing, SOAP and REST APIs communication, and web scraping), and database administration (MySQL and similar database data administration, data analytics, and reporting). By the end of this book, you will be able to use the latest features of Python and be able to build powerful tools that will solve challenging, real-world tasks What you will learn Understand how to install Python and debug Python scripts Understand and write scripts for automating testing and routine administrative activities Understand how to write scripts for text processing, encryption, decryption, and archiving Handle files, such as pdf, excel, csv, and txt files, and generate reports Write scripts for remote network administration, including handling emails Build interactive tools using a graphical user interface Handle Apache log files, SOAP and REST APIs communication Automate database administration and perform statistical analysis Who this book is for This book would be ideal for users with some basic understanding of Python programming and who are interested in scaling their programming skills to command line scripting and system administration. Prior ...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789133226/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 24
    Online Resource
    Online Resource
    Hands-on deep learning with Apache Spark : build and deploy distributed deep learning applications on Apache Spark (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788999700 , 1788999703
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Machine learning ; Electronic data processing ; Distributed processing ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Speed up the design and implementation of deep learning solutions using Apache Spark Key Features Explore the world of distributed deep learning with Apache Spark Train neural networks with deep learning libraries such as BigDL and TensorFlow Develop Spark deep learning applications to intelligently handle large and complex datasets Book Description Deep learning is a subset of machine learning where datasets with several layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts and the speed at which deep learning solutions can be implemented on Apache Spark. The book starts with the fundamentals of Apache Spark and deep learning. You will set up Spark for deep learning, learn principles of distributed modeling, and understand different types of neural nets. You will then implement deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) on Spark. As you progress through the book, you will gain hands-on experience of what it takes to understand the complex datasets you are dealing with. During the course of this book, you will use popular deep learning frameworks, such as TensorFlow, Deeplearning4j, and Keras to train your distributed models. By the end of this book, you'll have gained experience with the implementation of your models on a variety of use cases. What you will learn Understand the basics of deep learning Set up Apache Spark for deep learning Understand the principles of distribution modeling and different types of neural networks Obtain an understanding of deep learning algorithms Discover textual analysis and deep learning with Spark Use popular deep learning frameworks, such as Deeplearning4j, TensorFlow, and Keras Explore popular deep learning algorithms Who this book is for If you are a Scala developer, data scientist, or data analyst who wants to learn how to use Spark for implementing efficient deep learning models, Hands-On Deep Learning with Apache Spark is for you. Knowledge of the core machine learning concepts and some exposure to Spark will be helpful.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788994613/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 25
    Online Resource
    Online Resource
    Hands-on data science for marketing : improve your marketing strategies with machine learning using Python and R (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789348828 , 178934882X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Marketing ; Data processing ; Machine learning ; Marketing research ; Python (Computer program language) ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Optimize your marketing strategies through analytics and machine learning Key Features Understand how data science drives successful marketing campaigns Use machine learning for better customer engagement, retention, and product recommendations Extract insights from your data to optimize marketing strategies and increase profitability Book Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learn Learn how to compute and visualize marketing KPIs in Python and R Master what drives successful marketing campaigns with data science Use machine learning to predict customer engagement and lifetime value Make product recommendations that customers are most likely to buy Learn how to use A/B testing for better marketing decision making Implement machine learning to understand different customer segments Who this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-adv...
    Note: Description based on online resource; title from title page (Safari, viewed May 1, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789346343/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 26
    Online Resource
    Online Resource
    Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789347043 , 1789347041
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Mobile apps ; Application software ; Development ; Artificial intelligence ; Machine learning ; Mobile computing ; Electronic books ; Electronic books ; local
    Abstract: Learn to build end-to-end AI apps from scratch for Android and iOS using TensorFlow Lite, CoreML, and PyTorch Key Features Build practical, real-world AI projects on Android and iOS Implement tasks such as recognizing handwritten digits, sentiment analysis, and more Explore the core functions of machine learning, deep learning, and mobile vision Book Description We're witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision. This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms. By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users. What you will learn Explore the concepts and fundamentals of AI, deep learning, and neural networks Implement use cases for machine vision and natural language processing Build an ML model to predict car damage using TensorFlow Deploy TensorFlow on mobile to convert speech to text Implement GAN to recognize hand-written digits Develop end-to-end mobile applications that use AI principles Work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch Who this book is for Mobile Artificial Intelligence Projects is for machine learning professionals, deep learning engineers, AI engineers, and software engineers who want to integrate AI technology into mobile-based platforms and applications. Sound knowledge of machine learning and experience with any programming language is all you need to get started with this book.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 14, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 27
    Online Resource
    Online Resource
    Getting started with Python : understand key data structures and use Python in object-oriented programming (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781838559984 , 1838559981
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Learning path
    Keywords: Python (Computer program language) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Harness the power of Python objects and data structures to implement algorithms for analyzing your data and efficiently extracting information Key Features Turn your designs into working software by learning the Python syntax Write robust code with a solid understanding of Python data structures Understand when to use the functional or the OOP approach Book Description This Learning Path helps you get comfortable with the world of Python. It starts with a thorough and practical introduction to Python. You'll quickly start writing programs, building websites, and working with data by harnessing Python's renowned data science libraries. With the power of linked lists, binary searches, and sorting algorithms, you'll easily create complex data structures, such as graphs, stacks, and queues. After understanding cooperative inheritance, you'll expertly raise, handle, and manipulate exceptions. You will effortlessly integrate the object-oriented and not-so-object-oriented aspects of Python, and create maintainable applications using higher level design patterns. Once you've covered core topics, you'll understand the joy of unit testing and just how easy it is to create unit tests. By the end of this Learning Path, you will have built components that are easy to understand, debug, and can be used across different applications. This Learning Path includes content from the following Packt products: Learn Python Programming - Second Edition by Fabrizio Romano Python Data Structures and Algorithms by Benjamin Baka Python 3 Object-Oriented Programming by Dusty Phillips What you will learn Use data structures and control flow to write code Use functions to bundle together a sequence of instructions Implement objects in Python by creating classes and defining methods Design public interfaces using abstraction, encapsulation and information hiding Raise, define, and manipulate exceptions using special error objects Create bulletproof and reliable software by writing unit tests Learn the common programming patterns and algorithms used in Python Who this book is for If you are relatively new to coding and want to write scripts or programs to accomplish tasks using Python, or if you are an object-oriented programmer for other languages and seeking a leg up in the world of Python, then this Learning Path is for you. Though not essential, it will help you to have basic knowledge of programming and OOP.
    Note: Description based on online resource; title from title page (Safari, viewed April 24, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781838551919/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 28
    Online Resource
    Online Resource
    Python machine learning cookbook : over 100 recipes to progress from smart data analytics to deep learning using real-world datasets (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789800753 , 1789800757
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Key Features Learn and implement machine learning algorithms in a variety of real-life scenarios Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques Find easy-to-follow code solutions for tackling common and not-so-common challenges Book Description This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. What you will learn Use predictive modeling and apply it to real-world problems Explore data visualization techniques to interact with your data Learn how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Get well versed with reinforcement learning, automated ML, and transfer learning Work with image data and build systems for image recognition and biometric face recognition Use deep neural networks to build an optical character recognition system Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is w...
    Note: Previous edition published: 2016. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789808452/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 29
    Online Resource
    Online Resource
    Mastering machine learning with R : advanced machine learning techniques for building smart applications with R 3.5 (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789613568 , 1789613566
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition.
    Keywords: Machine learning ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications Key Features Build independent machine learning (ML) systems leveraging the best features of R 3.5 Understand and apply different machine learning techniques using real-world examples Use methods such as multi-class classification, regression, and clustering Book Description Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You'll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you'll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You'll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you'll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you'll be equipped with the skills to deploy ML techniques in your own projects or at work. What you will learn Prepare data for machine learning methods with ease Understand how to write production-ready code and package it for use Produce simple and effective data visualizations for improved insights Master advanced methods, such as Boosted Trees and deep neural networks Use natural language processing to extract insights in relation to text Implement tree-based classifiers, including Random Forest and Boosted Tree Who this book is for This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement...
    Note: Previous edition published: 2017. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789618006/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 30
    Online Resource
    Online Resource
    Hands-on Java deep learning for computer vision : implement machine learning and neural network methodologies to perform computer vision-related tasks (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781838552138 , 1838552138
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Neural networks (Computer science) ; Java (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Leverage the power of Java and deep learning to build production-grade Computer Vision applications Key Features Build real-world Computer Vision applications using the power of neural networks Implement image classification, object detection, and face recognition Know best practices on effectively building and deploying deep learning models in Java Book Description Although machine learning is an exciting world to explore, you may feel confused by all of its theoretical aspects. As a Java developer, you will be used to telling the computer exactly what to do, instead of being shown how data is generated; this causes many developers to struggle to adapt to machine learning. The goal of this book is to walk you through the process of efficiently training machine learning and deep learning models for Computer Vision using the most up-to-date techniques. The course is designed to familiarize you with neural networks, enabling you to train them efficiently, customize existing state-of-the-art architectures, build real-world Java applications, and get great results in a short space of time. You will build real-world Computer Vision applications, ranging from a simple Java handwritten digit recognition model to real-time Java autonomous car driving systems and face recognition models. By the end of this book, you will have mastered the best practices and modern techniques needed to build advanced Computer Vision Java applications and achieve production-grade accuracy. What you will learn Discover neural Networks and their applications in Computer Vision Explore the popular Java frameworks and libraries for deep learning Build deep neural networks in Java Implement an end-to-end image classification application in Java Perform real-time video object detection using deep learning Enhance performance and deploy applications for production Who this book is for This book is for data scientists, machine learning developers and deep learning practitioners with Java knowledge who want to implement machine learning and deep neural networks in the computer vision domain. You will need to have a basic knowledge of Java programming.
    Note: Description based on online resource; title from title page (Safari, viewed April 2, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789613964/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 31
    Online Resource
    Online Resource
    Hands-on machine learning with IBM Watson : leverage IBM Watson to implement machine learning techniques and algorithms using Python (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789616279 , 1789616271
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Python (Computer program language) ; Watson (Computer) ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services Key Features Implement data science and machine learning techniques to draw insights from real-world data Understand what IBM Cloud platform can help you to implement cognitive insights within applications Understand the role of data representation and feature extraction in any machine learning system Book Description IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies. By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples. What you will learn Understand key characteristics of IBM machine learning services Run supervised and unsupervised techniques in the cloud Understand how to create a Spark pipeline in Watson Studio Implement deep learning and neural networks on the IBM Cloud with TensorFlow Create a complete, cloud-based facial expression classification solution Use biometric traits to build a cloud-based human identification system Who this book is for This beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789611854/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 32
    Online Resource
    Online Resource
    TensorFlow reinforcement learning quick start guide : get up and running with training and deploying intelligent, self-learning agents using Python (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789533446 , 1789533449
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: TensorFlow ; Reinforcement learning ; Neural networks (Computer science) ; Python (Computer program language) ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key Features Explore efficient Reinforcement Learning algorithms and code them using TensorFlow and Python Train Reinforcement Learning agents for problems, ranging from computer games to autonomous driving. Formulate and devise selective algorithms and techniques in your applications in no time. Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learn Understand the theory and concepts behind modern Reinforcement Learning algorithms Code state-of-the-art Reinforcement Learning algorithms with discrete or continuous actions Develop Reinforcement Learning algorithms and apply them to training agents to play computer games Explore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlow Use A3C to play CartPole and LunarLander Train an agent to drive a car autonomously in a simulator Who this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 14, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789533583/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 33
    Online Resource
    Online Resource
    Mastering geospatial development with QGIS 3.x : an in-depth guide to becoming proficient in spatial data analysis using QGIS 3.4 and 3.6 with Python (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788994873 , 1788994876
    Language: English
    Pages: 1 online resource (1 volume) , illustrations, maps
    Edition: Third edition.
    Keywords: Geographic information systems ; Computer programs ; Geodatabases ; Computer programs ; Cartography ; Computer programs ; Geospatial data ; Data processing ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Go beyond the basics and unleash the full power of QGIS 3.4 and 3.6 with practical, step-by-step examples Key Features One-stop solution to all of your GIS needs Master QGIS by learning about database integration, and geoprocessing tools Learn about the new and updated Processing toolbox and perform spatial analysis Book Description QGIS is an open source solution to GIS and widely used by GIS professionals all over the world. It is the leading alternative to proprietary GIS software. Although QGIS is described as intuitive, it is also, by default, complex. Knowing which tools to use and how to apply them is essential to producing valuable deliverables on time. Starting with a refresher on the QGIS basics and getting you acquainted with the latest QGIS 3.6 updates, this book will take you all the way through to teaching you how to create a spatial database and a GeoPackage. Next, you will learn how to style raster and vector data by choosing and managing different colors. The book will then focus on processing raster and vector data. You will be then taught advanced applications, such as creating and editing vector data. Along with that, you will also learn about the newly updated Processing Toolbox, which will help you develop the advanced data visualizations. The book will then explain to you the graphic modeler, how to create QGIS plugins with PyQGIS, and how to integrate Python analysis scripts with QGIS. By the end of the book, you will understand how to work with all aspects of QGIS and will be ready to use it for any type of GIS work. What you will learn Create and manage a spatial database Get to know advanced techniques to style GIS data Prepare both vector and raster data for processing Add heat maps, live layer effects, and labels to your maps Master LAStools and GRASS integration with the Processing Toolbox Edit and repair topological data errors Automate workflows with batch processing and the QGIS Graphical Modeler Integrate Python scripting into your data processing workflows Develop your own QGIS plugins Who this book is for If you are a GIS professional, a consultant, a student, or perhaps a fast learner who wants to go beyond the basics of QGIS, then this book is for you. It will prepare you to realize the full potential of QGIS.
    Note: Previous edition published: 2016. - Description based on online resource; title from title page (Safari, viewed May 1, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788999892/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 34
    Online Resource
    Online Resource
    Python network programming : conquer all your networking challenges with the powerful Python language (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788830232 , 1788830237
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Computer networks ; Management ; Client/server computing ; Electronic books ; Electronic books ; local
    Abstract: Power up your network applications with Python programming Key Features Master Python skills to develop powerful network applications Grasp the fundamentals and functionalities of SDN Design multi-threaded, event-driven architectures for echo and chat servers Book Description This Learning Path highlights major aspects of Python network programming such as writing simple networking clients, creating and deploying SDN and NFV systems, and extending your network with Mininet. You'll also learn how to automate legacy and the latest network devices. As you progress through the chapters, you'll use Python for DevOps and open source tools to test, secure, and analyze your network. Toward the end, you'll develop client-side applications, such as web API clients, email clients, SSH, and FTP, using socket programming. By the end of this Learning Path, you will have learned how to analyze a network's security vulnerabilities using advanced network packet capture and analysis techniques. This Learning Path includes content from the following Packt products: Practical Network Automation by Abhishek Ratan Mastering Python Networking by Eric Chou Python Network Programming Cookbook, Second Edition by Pradeeban Kathiravelu, Dr. M. O. Faruque Sarker What you will learn Create socket-based networks with asynchronous models Develop client apps for web APIs, including S3 Amazon and Twitter Talk to email and remote network servers with different protocols Integrate Python with Cisco, Juniper, and Arista eAPI for automation Use Telnet and SSH connections for remote system monitoring Interact with websites via XML-RPC, SOAP, and REST APIs Build networks with Ryu, OpenDaylight, Floodlight, ONOS, and POX Configure virtual networks in different deployment environments Who this book is for If you are a Python developer or a system administrator who wants to start network programming, this Learning Path gets you a step closer to your goal. IT professionals and DevOps engineers who are new to managing network devices or those with minimal experience looking to expand their knowledge and skills in Python will also find this Learning Path useful. Although prior knowledge of networking is not required, some experience in Python programming will be helpful for a better understanding of the concepts in the Learning Path.
    Note: Description based on online resource; title from title page (Safari, viewed March 19, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788835466/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 35
    Online Resource
    Online Resource
    Hands-on big data analytics with PySpark : analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781838648831 , 1838648836
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: SPARK (Computer program language) ; Application software ; Development ; Big data ; Electronic data processing ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs Key Features Work with large amounts of agile data using distributed datasets and in-memory caching Source data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3 Employ the easy-to-use PySpark API to deploy big data Analytics for production Book Description Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively. What you will learn Get practical big data experience while working on messy datasets Analyze patterns with Spark SQL to improve your business intelligence Use PySpark's interactive shell to speed up development time Create highly concurrent Spark programs by leveraging immutability Discover ways to avoid the most expensive operation in the Spark API: the shuffle operation Re-design your jobs to use reduceByKey instead of groupBy Create robust processing pipelines by testing Apache Spark jobs Who this book is for This book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to mag...
    Note: Description based on online resource; title from title page (Safari, viewed May 9, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781838644130/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 36
    Online Resource
    Online Resource
    Data wrangling with Python (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789804249 , 1789804248
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Data mining ; Electronic data processing ; Information visualization ; Electronic books ; local ; Electronic books
    Abstract: Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices. Key Features Focus on the basics of data wrangling Study various ways to extract the most out of your data in less time Boost your learning curve with bonus topics like random data generation and data integrity checks Book Description For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. What you will learn Use and manipulate complex and simple data structures Harness the full potential of DataFrames and numpy.array at run time Perform web scraping with BeautifulSoup4 and html5lib Execute advanced string search and manipulation with RegEX Handle outliers and perform data imputation with Pandas Use descriptive statistics and plotting techniques Practice data wrangling and modeling using data generation techniques Who this book is for Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL.
    Note: Description based on online resource; title from title page (Safari, viewed April 18, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 37
    Online Resource
    Online Resource
    TensorFlow 2.0 quick start guide : get up to speed with the newly introduced features of TensorFlow 2.0 (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789536966 , 1789536960
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: TensorFlow ; Application software ; Development ; Neural networks (Computer science) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features Train your own models for effective prediction, using high-level Keras API Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha Book Description TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques. What you will learn Use tf.Keras for fast prototyping, building, and training deep learning neural network models Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications Understand image recognition techniques using TensorFlow Perform neural style transfer for image hybridization using a neural network Code a recurrent neural network in TensorFlow to perform text-style generation Who this book is for Data scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.
    Note: Description based on online resource; title from title page (Safari, viewed May 1, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789530759/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 38
    Online Resource
    Online Resource
    Apache Spark quick start guide : quickly learn the art of writing efficient big data applications with Apache Spark (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789342666 , 178934266X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Electronic data processing ; Distributed processing ; Management ; Big data ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: A practical guide for solving complex data processing challenges by applying the best optimizations techniques in Apache Spark. Key Features Learn about the core concepts and the latest developments in Apache Spark Master writing efficient big data applications with Spark's built-in modules for SQL, Streaming, Machine Learning and Graph analysis Get introduced to a variety of optimizations based on the actual experience Book Description Apache Spark is a flexible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases. It will also introduce you to Apache Spark ? one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark's built-in modules for SQL, streaming, machine learning, and graph analysis. Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications. What you will learn Learn core concepts such as RDDs, DataFrames, transformations, and more Set up a Spark development environment Choose the right APIs for your applications Understand Spark's architecture and the execution flow of a Spark application Explore built-in modules for SQL, streaming, ML, and graph analysis Optimize your Spark job for better performance Who this book is for If you are a big data enthusiast and love processing huge amount of data, this book is for you. If you are data engineer and looking for the best optimization techniques for your Spark applications, then you will find this book helpful. This book also helps data scientists who want to implement their mach...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789349108/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 39
    Online Resource
    Online Resource
    Python deep learning : exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789349702 , 1789349702
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book Description With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You'll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications. What you will learn Grasp the mathematical theory behind neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks Solve generative tasks using variational autoencoders and Generative Adversarial Networks Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models Explore reinforcement learning and understand how agents behave in a complex environment Get up to date with applications of deep learning in autonomous vehicles Who this book is for This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understandi...
    Note: Description based on online resource; title from title page (Safari, viewed February 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789348460/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 40
    Online Resource
    Online Resource
    Intelligent projects using Python : 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788994866 , 1788994868
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Artificial intelligence ; Machine learning ; Neural networks (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Implement machine learning and deep learning methodologies to build smart, cognitive AI projects using Python Key Features A go-to guide to help you master AI algorithms and concepts 8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillance Use TensorFlow, Keras, and other Python libraries to implement smart AI applications Book Description This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle. What you will learn Build an intelligent machine translation system using seq-2-seq neural translation machines Create AI applications using GAN and deploy smart mobile apps using TensorFlow Translate videos into text using CNN and RNN Implement smart AI Chatbots, and integrate and extend them in several domains Create smart reinforcement, learning-based applications using Q-Learning Break and generate CAPTCHA using Deep Learning and Adversarial Learning Who this book is for This book is intended for data scientists, machine learning professionals, and deep learning practitioners who are ready to extend their knowledge and potential in AI. If you want to build real-life smart systems to play a crucial role in every complex domain, then this book is w...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788996921/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 41
    Online Resource
    Online Resource
    Python machine learning blueprints : put your machine learning concepts to the test by developing real-world smart projects (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788997775 , 1788997778
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras Key Features Get to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and Keras Implement advanced concepts and popular machine learning algorithms in real-world projects Build analytics, computer vision, and neural network projects Book Description Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects. The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you'll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you'll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you'll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you'll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks. By the end of this book, you'll be able to analyze data seamlessly and make a powerful impact through your projects. What you will learn Understand the Python data science stack and commonly used algorithms Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window Understand NLP concepts by creating a custom news feed Create applications that will recommend GitHub repositories based on ones you've starred, watched, or forked Gain the skills to build a chatbot from scratch using PySpark Develop a market-prediction app using stock data Delve into advanced concepts such as computer vision, neural networks, and deep learning Who this book is for This book is for machine learning practitioners, data scientists, and...
    Note: Previous edition published: 2016. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788994170/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 42
    Online Resource
    Online Resource
    Natural language processing fundamentals (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789955989 , 178995598X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Natural language processing (Computer science) ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Use Python and NLTK (Natural Language Toolkit) to build out your own text classifiers and solve common NLP problems. Key Features Assimilate key NLP concepts and terminologies Explore popular NLP tools and techniques Gain practical experience using NLP in application code Book Description If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language. What you will learn Obtain, verify, and clean data before transforming it into a correct format for use Perform data analysis and machine learning tasks using Python Understand the basics of computational linguistics Build models for general natural language processing tasks Evaluate the performance of a model with the right metrics Visualize, quantify, and perform exploratory analysis from any text data Who this book is for Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. It'll help you to have prior experience of coding in Python using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but not necessary.
    Note: Description based on online resource; title from cover (Safari, viewed May 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789954043/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 43
    Online Resource
    Online Resource
    Machine learning quick reference : quick and essential machine learning hacks for training smart data models (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788831611 , 1788831616
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Your hands-on reference guide to developing, training, and optimizing your machine learning models Key Features Your guide to learning efficient machine learning processes from scratch Explore expert techniques and hacks for a variety of machine learning concepts Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems Book Description Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference. What you will learn Get a quick rundown of model selection, statistical modeling, and cross-validation Choose the best machine learning algorithm to solve your problem Explore kernel learning, neural networks, and time-series analysis Train deep learning models and optimize them for maximum performance Briefly cover Bayesian techniques and sentiment analysis in your NLP solution Implement probabilistic graphical models and causal inferences Measure and optimize the performance of your machine learning models Who this book is for If you're a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you're an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You'll need some exposure to machine learning to get the best out of this book.
    Note: Description based on online resource; title from title page (Safari, viewed March 19, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788830577/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 44
    Online Resource
    Online Resource
    Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781838647056 , 1838647058
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; R (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more. Key Features Use R 3.5 to implement real-world examples in machine learning Implement key machine learning algorithms to understand the working mechanism of smart models Create end-to-end machine learning pipelines using modern libraries from the R ecosystem Book Description Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R. What you will learn Introduce yourself to the basics of machine learning with R 3.5 Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results Learn to build predictive models with the help of various machine learning techniques Use R to visualize data spread across multiple dimensions and extract useful features Use interactive data analysis with R to get insights into data Implement supervised and unsupervised learning, and NLP using R libraries Who this book is for This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.
    Note: Description based on online resource; title from title page (Safari, viewed May 9, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781838644338/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 45
    Online Resource
    Online Resource
    Learning Python networking : a complete guide to build and deploy strong networking capabilities using Python 3.7 and Ansible (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789952445 , 1789952441
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; Computer networks ; Management ; Client/server computing ; Electronic books ; Electronic books ; local
    Abstract: Achieve improved network programmability and automation by leveraging powerful network programming concepts, algorithms, and tools Key Features Deal with remote network servers using SSH, FTP, SNMP and LDAP protocols. Design multi threaded and event-driven architectures for asynchronous servers programming. Leverage your Python programming skills to build powerful network applications Book Description Network programming has always been a demanding task. With full-featured and well-documented libraries all the way up the stack, Python makes network programming the enjoyable experience it should be. Starting with a walk through of today's major networking protocols, through this book, you'll learn how to employ Python for network programming, how to request and retrieve web resources, and how to extract data in major formats over the web. You will utilize Python for emailing using different protocols, and you'll interact with remote systems and IP and DNS networking. You will cover the connection of networking devices and configuration using Python 3.7, along with cloud-based network management tasks using Python. As the book progresses, socket programming will be covered, followed by how to design servers, and the pros and cons of multithreaded and event-driven architectures. You'll develop practical clientside applications, including web API clients, email clients, SSH, and FTP. These applications will also be implemented through existing web application frameworks. What you will learn Execute Python modules on networking tools Automate tasks regarding the analysis and extraction of information from a network Get to grips with asynchronous programming modules available in Python Get to grips with IP address manipulation modules using Python programming Understand the main frameworks available in Python that are focused on web application Manipulate IP addresses and perform CIDR calculations Who this book is for If you're a Python developer or a system administrator with Python experience and you're looking to take your first steps in network programming, then this book is for you. If you're a network engineer or a network professional aiming to be more productive and efficient in networking programmability and automation then this book would serve as a useful resource. Basic knowledge of Python is assumed.
    Note: Previous edition published: 2015. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789958096/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 46
    Online Resource
    Online Resource
    Mastering OpenCV 4 with Python : a practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7 (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789349757 , 1789349753
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: OpenCV (Computer program language) ; Computer vision ; Python (Computer program language) ; Image processing ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality. Key Features Develop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4)and Python Apply machine learning and deep learning techniques with TensorFlow, Keras, and PyTorch Discover the modern design patterns you should avoid when developing efficient computer vision applications Book Description OpenCV is considered to be one of the best open source computer vision and machine learning software libraries. It helps developers build complete projects in relation to image processing, motion detection, or image segmentation, among many others. OpenCV for Python enables you to run computer vision algorithms smoothly in real time, combining the best of the OpenCV C++ API and the Python language. In this book, you'll get started by setting up OpenCV and delving into the key concepts of computer vision. You'll then proceed to study more advanced concepts and discover the full potential of OpenCV. The book will also introduce you to the creation of advanced applications using Python and OpenCV, enabling you to develop applications that include facial recognition, target tracking, or augmented reality. Next, you'll learn machine learning techniques and concepts, understand how to apply them in real-world examples, and also explore their benefits, including real-time data production and faster data processing. You'll also discover how to translate the functionality provided by OpenCV into optimized application code projects using Python bindings. Toward the concluding chapters, you'll explore the application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras. By the end of this book, you'll be able to develop advanced computer vision applications to meet your customers' demands. What you will learn Handle files and images, and explore various image processing techniques Explore image transformations, including translation, resizing, and cropping Gain insights into building histograms Brush up on contour detection, filtering, and drawing Work with Augmented Reality to build marker-based and markerless applications Work with the main machine learning algorithms in OpenCV Explore the deep learning Python libraries and OpenCV deep learning capabilities Create computer vision and deep lear...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 1, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789344912/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 47
    Online Resource
    Online Resource
    Hands-on Markov models with Python : implement probabilistic models for learning complex data sequences using Python ecosystem (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788629331 , 1788629337 , 9781788625449
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    DDC: 005.133
    Keywords: Markov processes Numerical solutions ; Python (Computer program language) ; Machine learning ; Natural language processing (Computer science) ; Artificial intelligence ; Neural networks & fuzzy systems ; Natural language & machine translation ; Computers ; Intelligence (AI) & Semantics ; Computers ; Neural Networks ; Computers ; Natural Language Processing ; Machine learning ; Markov processes ; Numerical solutions ; Natural language processing (Computer science) ; Python (Computer program language) ; Electronic books
    Note: Online resource; title from title page (Safari, viewed November 2, 2018)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 48
    Online Resource
    Online Resource
    Artificial intelligence and machine learning fundamentals (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789809206 , 1789809207
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Artificial intelligence ; Neural networks (Computer science) ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Create AI applications in Python and lay the foundations for your career in data science Key Features Practical examples that explain key machine learning algorithms Explore neural networks in detail with interesting examples Master core AI concepts with engaging activities Book Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learn Understand the importance, principles, and fields of AI Implement basic artificial intelligence concepts with Python Apply regression and classification concepts to real-world problems Perform predictive analysis using decision trees and random forests Carry out clustering using the k-means and mean shift algorithms Understand the fundamentals of deep learning via practical examples Who this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it's recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).
    Note: Description based on online resource; title from copyright page (Safari, viewed February 11, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789801651/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 49
    Online Resource
    Online Resource
    Python artificial intelligence projects for beginners : get up and running with artificial intelligence using 8 smart and exciting AI applications (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789538243 , 1789538246
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Artificial intelligence ; Data processing ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Build smart applications by implementing real-world artificial intelligence projects Key Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI projects Book Description Artificial Intelligence (AI) is the newest technology that's being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress What you will learn Build a prediction model using decision trees and random forest Use neural networks, decision trees, and random forests for classification Detect YouTube comment spam with a bag-of-words and random forests Identify handwritten mathematical symbols with convolutional neural networks Revise the bird species identifier to use images Learn to detect positive and negative sentiment in user reviews Who this book is for Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you're able to play around with code Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.
    Note: Description based on online resource; title from title page (Safari, viewed August 28, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789539462/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 50
    Online Resource
    Online Resource
    Data science with SQL server quick start guide : integrate SQL server with data science (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789537130 , 1789537134
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: SQL server ; Client/server computing ; Machine learning ; Data mining ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Get unique insights from your data by combining the power of SQL Server, R and Python Key Features Use the features of SQL Server 2017 to implement the data science project life cycle Leverage the power of R and Python to design and develop efficient data models find unique insights from your data with powerful techniques for data preprocessing and analysis Book Description SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm. What you will learn Use the popular programming languages,T-SQL, R, and Python, for data science Understand your data with queries and introductory statistics Create and enhance the datasets for ML Visualize and analyze data using basic and advanced graphs Explore ML using unsupervised and supervised models Deploy models in SQL Server and perform predictions Who this book is for SQL Server professionals who want to start with data science, and data scientists who would like to start using SQL Server in their projects will find this book to be useful. Prior exposure to SQL Server will be helpful.
    Note: Description based on online resource; title from title page (Safari, viewed October 2, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789537123/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 51
    Online Resource
    Online Resource
    Recurrent neural networks with Python Quick Start Guide : sequential learning and language modeling with TensorFlow (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789133660 , 1789133661
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Neural networks (Computer science) ; Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key Features Train and deploy Recurrent Neural Networks using the popular TensorFlow library Apply long short-term memory units Expand your skills in complex neural network and deep learning topics Book Description Developers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will learn Use TensorFlow to build RNN models Use the correct RNN architecture for a particular machine learning task Collect and clear the training data for your models Use the correct Python libraries for any task during the building phase of your model Optimize your model for higher accuracy Identify the differences between multiple models and how you can substitute them Learn the core deep learning fundamentals applicable to any machine learning model Who this book is for This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (viewed February 5, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789132335/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 52
    Online Resource
    Online Resource
    Modern Python standard library cookbook : over 100 recipes to fully leverage the features of the standard library in Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788833295 , 1788833295
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Build optimized applications in Python by smartly implementing the standard library Key Features Strategic recipes for effective application development in Python Techniques to create GUIs and implement security through cryptography Best practices for developing readily scalable, production-ready applications Book Description The Python 3 Standard Library is a vast array of modules that you can use for developing various kinds of applications. It contains an exhaustive list of libraries, and this book will help you choose the best one to address specific programming problems in Python. The Modern Python Standard Library Cookbook begins with recipes on containers and data structures and guides you in performing effective text management in Python. You will find Python recipes for command-line operations, networking, filesystems and directories, and concurrent execution. You will learn about Python security essentials in Python and get to grips with various development tools for debugging, benchmarking, inspection, error reporting, and tracing. The book includes recipes to help you create graphical user interfaces for your application. You will learn to work with multimedia components and perform mathematical operations on date and time. The recipes will also show you how to deploy different searching and sorting algorithms on your data. By the end of the book, you will have acquired the skills needed to write clean code in Python and develop applications that meet your needs. What you will learn Store multiple values per key in associative containers Create interactive character-based user interfaces Work with native time and display data for your time zone Read/write SGML family languages, both as a SAX and DOM parser to meet file sizes and other requirements Group equivalent items using itertools and sorted features together Use partials to create unary functions out of multi-argument functions Implement hashing algorithms to store passwords in a safe way Who this book is for If you are a developer who wants to write highly responsive, manageable, scalable, and resilient code in Python, this book is for you. Prior programming knowledge in Python will help you make the most out of the book.
    Note: Description based on online resource; title from title page (Safari, viewed October 1, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788830829/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 53
    Online Resource
    Online Resource
    TensorFlow deep learning projects : 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788398381 , 1788398386
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios About This Book Build efficient deep learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects related to Computer Vision, stock prediction, chatbots and more Who This Book Is For This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book. What You Will Learn Set up the TensorFlow environment for deep learning Construct your own ConvNets for effective image processing Use LSTMs for image caption generation Forecast stock prediction accurately with an LSTM architecture Learn what semantic matching is by detecting duplicate Quora questions Set up an AWS instance with TensorFlow to train GANs Train and set up a chatbot to understand and interpret human input Build an AI capable of playing a video game by itself ?and win it! In Detail TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. Style and approach This book contains 10 unique, end-to-end projects covering all aspects of ...
    Note: Description based on online resource; title from title page (Safari, viewed April 19, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788398060/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 54
    Online Resource
    Online Resource
    IPython interactive computing and visualization cookbook : over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science in the Jupyter Notebook (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785881930 , 1785881930
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; Command languages (Computer science) ; Information visualization ; Interactive computer systems ; Electronic books ; Electronic books ; local
    Abstract: Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. About This Book Leverage the Jupyter Notebook for interactive data science and visualization Become an expert in high-performance computing and visualization for data analysis and scientific modeling A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations Who This Book Is For This book is intended for anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, and hobbyists. A basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods. What You Will Learn Master all features of the Jupyter Notebook Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments Visualize data and create interactive plots in the Jupyter Notebook Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn) Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV Simulate deterministic and stochastic dynamical systems in Python Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory In Detail Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed February 22, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781785888632/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 55
    Online Resource
    Online Resource
    Pytest quick start guide : write better Python code with simple and maintainable tests (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789343823 , 1789343828
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Computer software ; Testing ; Debugging in computer science ; Electronic books ; Electronic books ; local
    Abstract: Learn the pytest way to write simple tests which can also be used to write complex tests Key Features Become proficient with pytest from day one by solving real-world testing problems Use pytest to write tests more efficiently Scale from simple to complex and functional testing Book Description Python's standard unittest module is based on the xUnit family of frameworks, which has its origins in Smalltalk and Java, and tends to be verbose to use and not easily extensible.The pytest framework on the other hand is very simple to get started, but powerful enough to cover complex testing integration scenarios, being considered by many the true Pythonic approach to testing in Python. In this book, you will learn how to get started right away and get the most out of pytest in your daily work?ow, exploring powerful mechanisms and plugins to facilitate many common testing tasks. You will also see how to use pytest in existing unittest-based test suites and will learn some tricks to make the jump to a pytest-style test suite quickly and easily. What you will learn Write and run simple and complex tests Organize tests in fles and directories Find out how to be more productive on the command line Markers and how to skip, xfail and parametrize tests Explore fxtures and techniques to use them effectively, such as tmpdir, pytestconfg, and monkeypatch Convert unittest suites to pytest using little-known techniques Use third-party plugins Who this book is for This book is for Python programmers that want to learn more about testing. This book is also for QA testers, and those who already benefit from programming with tests daily but want to improve their existing testing tools.
    Note: Description based on online resource; title from title page (Safari, viewed September 20, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789347562/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 56
    Online Resource
    Online Resource
    Bayesian analysis with Python : introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789349665 , 1789349664
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; Natural language processing (Computer science) ; Bayesian statistical decision theory ; Electronic books ; Electronic books ; local
    Abstract: Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises. Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learn Build probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and caveats of hierarchical models Find out how different models can be used to answer different data analysis questions Compare models and choose between alternative ones Discover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian framework Who this book is for If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.
    Note: Includes index. - Description based on online resource; title from title page (viewed February 13, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789341652/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 57
    Online Resource
    Online Resource
    Generative adversarial networks cookbook : over 100 recipes to build generative models using Python, TensorFlow, and Keras (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789139587 , 1789139589
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Neural networks (Computer science) ; Machine learning ; Python (Computer program language) ; Artificial intelligence ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and Keras Key Features Understand the common architecture of different types of GANs Train, optimize, and deploy GAN applications using TensorFlow and Keras Build generative models with real-world data sets, including 2D and 3D data Book Description Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away. What you will learn Structure a GAN architecture in pseudocode Understand the common architecture for each of the GAN models you will build Implement different GAN architectures in TensorFlow and Keras Use different datasets to enable neural network functionality in GAN models Combine different GAN models and learn how to fine-tune them Produce a model that can take 2D images and produce 3D models Develop a GAN to do style transfer with Pix2Pix Who this book is for This book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.
    Note: Description based on online resource; title from title page (Safari, viewed February 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789139907/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 58
    Online Resource
    Online Resource
    Hands-on predictive analytics with Python : master the complete predictive analytics process, from problem definition to model deployment (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789134544 , 1789134544
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Application software ; Development ; Decision making ; Data processing ; Data mining ; Big data ; Electronic books ; Electronic books ; local
    Abstract: Step-by-step guide to build high performing predictive applications Key Features Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Book Description Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming. What you will learn Get to grips with the main concepts and principles of predictive analytics Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Who this book is for This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this excitin...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 26, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789138719/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 59
    Online Resource
    Online Resource
    Clean code in Python : refactor your legacy code base (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788837064 , 1788837061
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Getting the most out of Python to improve your codebase Key Features Save maintenance costs by learning to fix your legacy codebase Learn the principles and techniques of refactoring Apply microservices to your legacy systems by implementing practical techniques Book Description Python is currently used in many different areas such as software construction, systems administration, and data processing. In all of these areas, experienced professionals can find examples of inefficiency, problems, and other perils, as a result of bad code. After reading this book, readers will understand these problems, and more importantly, how to correct them. The book begins by describing the basic elements of writing clean code and how it plays an important role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design. You will learn to implement the SOLID principles in Python and use decorators to improve your code. The book delves more deeply into object oriented programming in Python and shows you how to use objects with descriptors and generators. It will also show you the design principles of software testing and how to resolve software problems by implementing design patterns in your code. In the final chapter we break down a monolithic application to a microservice one, starting from the code as the basis for a solid platform. By the end of the book, you will be proficient in applying industry approved coding practices to design clean, sustainable and readable Python code. What you will learn Set up tools to effectively work in a development environment Explore how the magic methods of Python can help us write better code Examine the traits of Python to create advanced object-oriented design Understand removal of duplicated code using decorators and descriptors Effectively refactor code with the help of unit tests Learn to implement the SOLID principles in Python Who this book is for This book will appeal to team leads, software architects and senior software engineers who would like to work on their legacy systems to save cost and improve efficiency. A strong understanding of Programming is assumed. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and registe...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed September 19, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788835831/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 60
    Online Resource
    Online Resource
    Deep reinforcement learning hands-on : apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788839303 , 1788839307
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Reinforcement learning ; Machine learning ; Natural language processing (Computer science) ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. About This Book Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms Keep up with the very latest industry developments, including AI-driven chatbots Who This Book Is For Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL. What You Will Learn Understand the DL context of RL and implement complex DL models Learn the foundation of RL: Markov decision processes Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others Discover how to deal with discrete and continuous action spaces in various environments Defeat Atari arcade games using the value iteration method Create your own OpenAI Gym environment to train a stock trading agent Teach your agent to play Connect4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI-driven chatbots In Detail Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google's use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots. Style and approach Deep Reinforcement Learning Hands-On explains the art of building self-learning agents using algori...
    Note: "Expert insight.". - Includes bibliographical references and index. - Description based on online resource; title from cover (Safari, viewed July 30, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788834247/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 61
    Online Resource
    Online Resource
    Python robotics projects : build smart and collaborative robots using Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788837149 , 1788837142
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Robotics ; Robots ; Programming ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Leverage the power of Python to build DIY robotic projects About This Book Design, build, and stimulate collaborative robots Build high-end robotics projects such as a customized personal Jarvis Leverage the power of Python and ROS for DIY robotic projects Who This Book Is For If building robots is your dream, then this book is made for you. Prior knowledge of Python would be an added advantage. What You Will Learn Get to know the basics of robotics and its functions Walk through interface components with microcontrollers Integrate robotics with the IoT environment Build projects using machine learning Implement path planning and vision processing Interface your robots with Bluetooth In Detail Robotics is a fast-growing industry. Multiple surveys state that investment in the field has increased tenfold in the last 6 years, and is set to become a $100-billion sector by 2020. Robots are prevalent throughout all industries, and they are all set to be a part of our domestic lives. This book starts with the installation and basic steps in configuring a robotic controller. You'll then move on to setting up your environment to use Python with the robotic controller. You'll dive deep into building simple robotic projects, such as a pet-feeding robot, and more complicated projects, such as machine learning enabled home automation system (Jarvis), vision processing based robots and a self-driven robotic vehicle using Python. By the end of this book, you'll know how to build smart robots using Python. Style and approach A simple step-by-step guide to help you learn the concepts of robotics using simple to advanced steps. You'll not only learn the concepts of AI, machine learning, and Vision Processing, but also how to practically implement them in your projects.
    Note: Description based on online resource; title from title page (Safari, viewed June 20, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788832922/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 62
    Online Resource
    Online Resource
    Keras deep learning cookbook : over 30 recipes for implementing deep neural networks in Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788623087 , 1788623088
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Leverage the power of deep learning and Keras to develop smarter and more efficient data models Key Features Understand different neural networks and their implementation using Keras Explore recipes for training and fine-tuning your neural network models Put your deep learning knowledge to practice with real-world use-cases, tips, and tricks Book Description Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning What you will learn Install and configure Keras in TensorFlow Master neural network programming using the Keras library Understand the different Keras layers Use Keras to implement simple feed-forward neural networks, CNNs and RNNs Work with various datasets and models used for image and text classification Develop text summarization and reinforcement learning models using Keras Who this book is for Keras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 11, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788621755/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 63
    Online Resource
    Online Resource
    Apache Spark deep learning cookbook : over 80 recipes that streamline deep learning in a distributed environment with Apache Spark (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788471558 , 1788471555
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Machine learning ; Electronic data processing ; Distributed processing ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: A solution-based guide to put your deep learning models into production with the power of Apache Spark Key Features Discover practical recipes for distributed deep learning with Apache Spark Learn to use libraries such as Keras and TensorFlow Solve problems in order to train your deep learning models on Apache Spark Book Description With deep learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient deep learning libraries. As a result, this will help deep learning models train with higher efficiency and speed. With the help of the Apache Spark Deep Learning Cookbook, you'll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you'll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you'll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras. By the end of the book, you'll have the expertise to train and deploy efficient deep learning models on Apache Spark. What you will learn Set up a fully functional Spark environment Understand practical machine learning and deep learning concepts Apply built-in machine learning libraries within Spark Explore libraries that are compatible with TensorFlow and Keras Explore NLP models such as Word2vec and TF-IDF on Spark Organize dataframes for deep learning evaluation Apply testing and training modeling to ensure accuracy Access readily available code that may be reusable Who this book is for If you're looking for a practical and highly useful resource for implementing efficiently distributed deep learning models with Apache Spark, then the Apache Spark Deep Learning Cookbook is for you. Knowledge of the core machine learning concepts and a basic understanding of the Apache Spark framework is required to get the best out of this book. Additionally, some programming knowledge in Python ...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed August 15, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788474221/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 64
    Online Resource
    Online Resource
    Hands-on enterprise application development with Python : design data-intensive application with Python 3 (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789530636 , 1789530636
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Architect scalable, reliable, and maintainable applications for enterprises with Python Key Features Explore various Python design patterns used for enterprise software development Apply best practices for testing and performance optimization to build stable applications Learn about different attacking strategies used on enterprise applications and how to avoid them Book Description Dynamically typed languages like Python are continuously improving. With the addition of exciting new features and a wide selection of modern libraries and frameworks, Python has emerged as an ideal language for developing enterprise applications. Hands-On Enterprise Application Development with Python will show you how to build effective applications that are stable, secure, and easily scalable. The book is a detailed guide to building an end-to-end enterprise-grade application in Python. You will learn how to effectively implement Python features and design patterns that will positively impact your application lifecycle. The book also covers advanced concurrency techniques that will help you build a RESTful application with an optimized frontend. Given that security and stability are the foundation for an enterprise application, you'll be trained on effective testing, performance analysis, and security practices, and understand how to embed them in your codebase during the initial phase. You'll also be guided in how to move on from a monolithic architecture to one that is service oriented, leveraging microservices and serverless deployment techniques. By the end of the book, you will have become proficient at building efficient enterprise applications in Python. What you will learn Understand the purpose of design patterns and their impact on application lifecycle Build applications that can handle large amounts of data-intensive operations Uncover advanced concurrency techniques and discover how to handle a large number of requests in production Optimize frontends to improve the client-side experience of your application Effective testing and performance profiling techniques to detect issues in applications early in the development cycle Build applications with a focus on security Implement large applications as microservices to improve scalability Who this book is for If you're a developer who wants to build enterprise-grade applications, this book is for you. Basic to intermediate-level of programming experience with Python and database systems is required to ...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 26, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789532364/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 65
    Online Resource
    Online Resource
    Learn programming in Python with Cody Jackson : grasp the basics of programming and Python syntax while building real-world applications (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789533538 , 1789533538
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Kick-start your development journey with this end-to-end guide that covers Python programming fundamentals along with application development Key Features Gain a solid understanding of Python programming with coverage of data structures and Object-Oriented Programming (OOP) Design graphical user interfaces for desktops with libraries such as Kivy and Tkinter Write elegant, reusable, and efficient code Book Description Python is a cross-platform language used by organizations such as Google and NASA. It lets you work quickly and efficiently, allowing you to concentrate on your work rather than the language. Based on his personal experiences when learning to program, Learn Programming in Python with Cody Jackson provides a hands-on introduction to computer programming utilizing one of the most readable programming languages?Python. It aims to educate readers regarding software development as well as help experienced developers become familiar with the Python language, utilizing real-world lessons to help readers understand programming concepts quickly and easily. The book starts with the basics of programming, and describes Python syntax while developing the skills to make complete programs. In the first part of the book, readers will be going through all the concepts with short and easy-to-understand code samples that will prepare them for the comprehensive application built in parts 2 and 3. The second part of the book will explore topics such as application requirements, building the application, testing, and documentation. It is here that you will get a solid understanding of building an end-to-end application in Python. The next part will show you how to complete your applications by converting text-based simulation into an interactive, graphical user interface, using a desktop GUI framework. After reading the book, you will be confident in developing a complete application in Python, from program design to documentation to deployment. What you will learn Use the interactive shell for prototyping and code execution, including variable assignment Deal with program errors by learning when to manually throw exceptions Employ exceptions for code management Enhance code by utilizing Python's built-in shortcuts to improve efficiency and make coding easier Interact with files and package Python data for network transfer or storage Understand how tests drive code writing, and vice versa Explore the different frameworks that are available for GUI de...
    Note: Description based on online resource; title from title page (Safari, viewed March 8, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789531947/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 66
    Online Resource
    Online Resource
    Hands-on convolutional neural networks with TensorFlow : solve computer vision problems with modeling in TensorFlow and Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789132823 , 1789132827
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Neural networks (Computer science) ; Machine learning ; Artificial intelligence ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features Learn the fundamentals of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn Train machine learning models with TensorFlow Create systems that can evolve and scale during their life cycle Use CNNs in image recognition and classification Use TensorFlow for building deep learning models Train popular deep learning models Fine-tune a neural network to improve the quality of results with transfer learning Build TensorFlow models that can scale to large datasets and systems Who this book is for This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.
    Note: Description based on online resource; title from title page (Safari, viewed October 5, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789130331/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 67
    Online Resource
    Online Resource
    Feature engineering made easy : identify unique features from your dataset in order to build powerful machine learning systems (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787286474 , 1787286479
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Big data ; Electronic data processing ; Electronic books ; Electronic books ; local
    Abstract: A perfect guide to speed up the predicting power of machine learning algorithms About This Book Design, discover, and create dynamic, efficient features for your machine learning application Understand your data in-depth and derive astonishing data insights with the help of this Guide Grasp powerful feature-engineering techniques and build machine learning systems Who This Book Is For If you are a data science professional or a machine learning engineer looking to strengthen your predictive analytics model, then this book is a perfect guide for you. Some basic understanding of the machine learning concepts and Python scripting would be enough to get started with this book. What You Will Learn Identify and leverage different feature types Clean features in data to improve predictive power Understand why and how to perform feature selection, and model error analysis Leverage domain knowledge to construct new features Deliver features based on mathematical insights Use machine-learning algorithms to construct features Master feature engineering and optimization Harness feature engineering for real world applications through a structured case study In Detail Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data-often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. Style and approach This step-by-step guide with use cases, examples, and illustrations will help you master the concepts of feature engineering. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world. Downloading the example code for this book...
    Note: Description based on online resource; title from title page (Safari, viewed February 16, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781787287600/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 68
    Online Resource
    Online Resource
    Internet of Things programming projects : build modern IoT solutions with the Raspberry Pi 3 and Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789131383 , 1789131383
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Internet of things ; Application software ; Development ; Raspberry Pi (Computer) ; Python (Computer program language) ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: A practical project-based guide to help you build and control your IoT projects Key Features Leverage the full potential of IoT with the combination of Raspberry Pi 3 and Python Build complex Python-based applications with IoT Work on various IoT projects and understand the basics of electronics Book Description The Internet of Things (IOT) has managed to attract the attention of researchers and tech enthusiasts, since it powerfully combines classical networks with instruments and devices. In Internet of Things Programming Projects, we unleash the power of Raspberry Pi and Python to create engaging projects. In the first part of the book, you'll be introduced to the Raspberry Pi, learn how to set it up, and then jump right into Python programming. Then, you'll dive into real-world computing by creating a?Hello World? app using flash LEDs. As you make your way through the chapters, you'll go back to an age when analog needle meters ruled the world of data display. You'll learn to retrieve weather data from a web service and display it on an analog needle meter, and build a home security system using the Raspberry Pi. The next project has a modern twist, where we employ the Raspberry Pi to send a signal to a web service that will send you a text when someone is at the door. In the final project, you take what you've learned from the previous two projects and create an IoT robot car that you can use to monitor what your pets are up to when you are away. By the end of this book, you will be well versed in almost every possible way to make your IoT projects stand out. What you will learn Install and set up a Raspberry Pi for IoT development Learn how to use a servo motor as an analog needle meter to read data Build a home security dashboard using an infrared motion detector Communicate with a web service that sends you a message when the doorbell rings Receive data and display it with an actuator connected to the Raspberry Pi Build an IoT robot car that is controlled through the internet Who this book is for Internet of Things Programming Projects is for Python developers and programmers who are interested in building their own IoT applications and IoT-based projects. It is also targeted at IoT programmers and developers who are looking to build exciting projects with Python.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789134803/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 69
    Online Resource
    Online Resource
    Hands-on machine learning with JavaScript : solve complex computational web problems using machine learning (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788990301 , 1788990307
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: JavaScript (Computer program language) ; Machine learning ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript About This Book Solve complex computational problems in browser with JavaScript Teach your browser how to learn from rules using the power of machine learning Understand discoveries on web interface and API in machine learning Who This Book Is For This book is for you if you are a JavaScript developer who wants to implement machine learning to make applications smarter, gain insightful information from the data, and enter the field of machine learning without switching to another language. Working knowledge of JavaScript language is expected to get the most out of the book. What You Will Learn Get an overview of state-of-the-art machine learning Understand the pre-processing of data handling, cleaning, and preparation Learn Mining and Pattern Extraction with JavaScript Build your own model for classification, clustering, and prediction Identify the most appropriate model for each type of problem Apply machine learning techniques to real-world applications Learn how JavaScript can be a powerful language for machine learning In Detail In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier,...
    Note: Description based on online resource; title from title page (Safari, viewed June 20, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788998246/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 70
    Online Resource
    Online Resource
    Machine learning projects for mobile applications : build Android and iOS applications using TensorFlow Lite and Core ML (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788998468 , 1788998464
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: iOS (Electronic resource) ; Android (Electronic resource) ; Application software ; Development ; Mobile apps ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Bring magic to your mobile apps using TensorFlow Lite and Core ML Key Features Explore machine learning using classification, analytics, and detection tasks. Work with image, text and video datasets to delve into real-world tasks Build apps for Android and iOS using Caffe, Core ML and Tensorflow Lite Book Description Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so. The book starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google's ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN. By the end of this book, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML. What you will learn Demystify the machine learning landscape on mobile Age and gender detection using TensorFlow Lite and Core ML Use ML Kit for Firebase for in-text detection, face detection, and barcode scanning Create a digit classifier using adversarial learning Build a cross-platform application with face filters using OpenCV Classify food using deep CNNs and TensorFlow Lite on iOS Who this book is for Machine Learning Projects for Mobile Applications is for you if you are a data scientist, machine learning expert, deep learning, or AI enthusiast who fancies mastering machine learning and deep learning implementation with practical examples using TensorFlow Lite and CoreML. Basic knowledge of Python programming language would be an added advantage.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed December 11, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788994590/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 71
    Online Resource
    Online Resource
    Building machine learning systems with Python : explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788622226 , 1788622227
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition.
    Keywords: Python (Computer program language) ; Machine learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Get more from your data by creating practical machine learning systems with Python Key Features Develop your own Python-based machine learning system Discover how Python offers multiple algorithms for modern machine learning systems Explore key Python machine learning libraries to implement in your projects Book Description Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you'll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems. By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks. What you will learn Build a classification system that can be applied to text, images, and sound Employ Amazon Web Services (AWS) to run analysis on the cloud Solve problems related to regression using scikit-learn and TensorFlow Recommend products to users based on their past purchases Understand different ways to apply deep neural networks on structured data Address recent developments in the field of computer vision and reinforcement learning Who this book is for Building Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python progr...
    Note: Previous edition published: 2015. - Description based on online resource; title from title page (Safari, viewed August 27, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788623223/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 72
    Online Resource
    Online Resource
    Tkinter GUI programming by example : learn to create modern GUIs using Tkinter by building real-world projects in Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788622578 , 178862257X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Graphical user interfaces (Computer systems) Programming ; Web applications Development ; Python (Computer program language)
    Note: Description based on online resource; title from title page (Safari, viewed May 24, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788627481/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 73
    Online Resource
    Online Resource
    Jupyter cookbook : over 75 recipes to perform interactive computing across Python, R, Scala, Spark, JavaScript, and more (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788839747 , 1788839749
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    ST 232
    Keywords: Electronic data processing ; Data mining ; Information visualization ; Python (Computer program language) ; R (Computer program language) ; Scala (Computer program language)
    Note: Description based on online resource; title from title page (Safari, viewed May 31, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 74
    Online Resource
    Online Resource
    Learning Python web penetration testing : automate web penetration testing activities using Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789539677 , 1789539676
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Python (Computer program language) ; Penetration testing (Computer security) ; Computer networks Security measures
    Note: Description based on online resource; title from title page (Safari, viewed July 23, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781789533972/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 75
    Online Resource
    Online Resource
    Hands-on artificial intelligence with Java for beginners : build intelligent apps using machine learning and deep learning with Deeplearning4j (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789531022 , 1789531020
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Java (Computer program language) ; Artificial intelligence ; Computer programs ; Machine learning ; Problem solving ; Computer programs ; Electronic books ; Electronic books ; local
    Abstract: Build, train, and deploy intelligent applications using Java libraries Key Features Leverage the power of Java libraries to build smart applications Build and train deep learning models for implementing artificial intelligence Learn various algorithms to automate complex tasks Book Description Artificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data. AI can be used for automating systems or processes to carry out complex tasks and functions in order to achieve optimal performance and productivity. Hands-On Artificial Intelligence with Java for Beginners begins by introducing you to AI concepts and algorithms. You will learn about various Java-based libraries and frameworks that can be used in implementing AI to build smart applications. In addition to this, the book teaches you how to implement easy to complex AI tasks, such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation, all with a practical approach. By the end of this book, you will not only have a solid grasp of AI concepts, but you'll also be able to build your own smart applications for multiple domains. What you will learn Leverage different Java packages and tools such as Weka, RapidMiner, and Deeplearning4j, among others Build machine learning models using supervised and unsupervised machine learning techniques Implement different deep learning algorithms in Deeplearning4j and build applications based on them Study the basics of heuristic searching and genetic programming Differentiate between syntactic and semantic similarity among texts Perform sentiment analysis for effective decision making with LingPipe Who this book is for Hands-On Artificial Intelligence with Java for Beginners is for Java developers who want to learn the fundamentals of artificial intelligence and extend their programming knowledge to build smarter applications.
    Note: Description based on online resource; title from title page (Safari, viewed October 2, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789537550/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 76
    Online Resource
    Online Resource
    Machine learning for healthcare analytics projects : build smart AI applications using neural network methodologies across the healthcare vertical market (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789532524 , 1789532523
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Medical care ; Data processing ; Medical care ; Information services ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn Key Features Develop a range of healthcare analytics projects using real-world datasets Implement key machine learning algorithms using a range of libraries from the Python ecosystem Accomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologies Book Description Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics. This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks. By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain. What you will learn Explore super imaging and natural language processing (NLP) to classify DNA sequencing Detect cancer based on the cell information provided to the SVM Apply supervised learning techniques to diagnose autism spectrum disorder (ASD) Implement a deep learning grid and deep neural networks for detecting diabetes Analyze data from blood pressure, heart rate, and cholesterol level tests using neural networks Use ML algorithms to detect autistic disorders Who this book is for Machine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machi...
    Note: Description based on online resource; title from title page (viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789536591/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 77
    Online Resource
    Online Resource
    Become a Python data analyst : perform exploratory data analysis and gain insight into scientific computing using Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789534405 , 1789534402
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Data mining ; Python (Computer program language) ; Quantitative research ; Science ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Enhance your data analysis and predictive modeling skills using popular Python tools Key Features Cover all fundamental libraries for operation and manipulation of Python for data analysis Implement real-world datasets to perform predictive analytics with Python Access modern data analysis techniques and detailed code with scikit-learn and SciPy Book Description Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations. Become a Python Data Analyst introduces Python's most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations. In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end of this book, you will have hands-on experience performing data analysis with Python. What you will learn Explore important Python libraries and learn to install Anaconda distribution Understand the basics of NumPy Produce informative and useful visualizations for analyzing data Perform common statistical calculations Build predictive models and understand the principles of predictive analytics Who this book is for Become a Python Data Analyst is for entry-level data analysts, data engineers, and BI professionals who want to make complete use of Python tools for performing efficient data analysis. Prior knowledge of Python programming is necessary to understand the concepts covered in this book
    Note: Description based on online resource; title from title page (Safari, viewed October 2, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789531701/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 78
    Online Resource
    Online Resource
    Bioinformatics with Python cookbook : learn how to use modern Python bioinformatics libraries and applications to do cutting-edge research in computational biology (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789349986 , 1789349982
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Bioinformatics ; Python (Computer program language) ; Computational biology ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Discover modern, next-generation sequencing libraries from Python ecosystem to analyze large amounts of biological data Key Features Perform complex bioinformatics analysis using the most important Python libraries and applications Implement next-generation sequencing, metagenomics, automating analysis, population genetics, and more Explore various statistical and machine learning techniques for bioinformatics data analysis Book Description Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data. This book covers next-generation sequencing, genomics, metagenomics, population genetics, phylogenetics, and proteomics. You'll learn modern programming techniques to analyze large amounts of biological data. With the help of real-world examples, you'll convert, analyze, and visualize datasets using various Python tools and libraries. This book will help you get a better understanding of working with a Galaxy server, which is the most widely used bioinformatics web-based pipeline system. This updated edition also includes advanced next-generation sequencing filtering techniques. You'll also explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks such as Dask and Spark. By the end of this book, you'll be able to use and implement modern programming techniques and frameworks to deal with the ever-increasing deluge of bioinformatics data. What you will learn Learn how to process large next-generation sequencing (NGS) datasets Work with genomic dataset using the FASTQ, BAM, and VCF formats Learn to perform sequence comparison and phylogenetic reconstruction Perform complex analysis with protemics data Use Python to interact with Galaxy servers Use High-performance computing techniques with Dask and Spark Visualize protein dataset interactions using Cytoscape Use PCA and Decision Trees, two machine learning techniques, with biological datasets Who this book is for This book is for Data data Scientistsscientists, Bioinformatics bioinformatics analysts, researchers, and Python developers who want to address intermediate-to-advanced biological and bioinformatics problems using a recipe-based approach. Working knowledge of the Python programming language is expected.
    Note: Previous edition published: 2015. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 1, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789344691/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 79
    Online Resource
    Online Resource
    Mastering concurrency in Python : create faster programs using concurrency, asynchronous, multithreading, and parallel programming (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789341362 , 1789341361
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Immerse yourself in the world of Python concurrency and tackle the most complex concurrent programming problems Key Features Explore the core syntaxes, language features and modern patterns of concurrency in Python Understand how to use concurrency to keep data consistent and applications responsive Utilize application scaffolding to design highly-scalable programs Book Description Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. Concurrency and parallelism in Python are essential when it comes to multiprocessing and multithreading; they behave differently, but their common aim is to reduce the execution time. This book serves as a comprehensive introduction to various advanced concepts in concurrent engineering and programming. Mastering Concurrency in Python starts by introducing the concepts and principles in concurrency, right from Amdahl's Law to multithreading programming, followed by elucidating multiprocessing programming, web scraping, and asynchronous I/O, together with common problems that engineers and programmers face in concurrent programming. Next, the book covers a number of advanced concepts in Python concurrency and how they interact with the Python ecosystem, including the Global Interpreter Lock (GIL). Finally, you'll learn how to solve real-world concurrency problems through examples. By the end of the book, you will have gained extensive theoretical knowledge of concurrency and the ways in which concurrency is supported by the Python language What you will learn Explore the concepts of concurrency in programming Explore the core syntax and features that enable concurrency in Python Understand the correct way to implement concurrency Abstract methods to keep the data consistent in your program Analyze problems commonly faced in concurrent programming Use application scaffolding to design highly-scalable programs Who this book is for This book is for developers who wish to build high-performance applications and learn about signle-core, multicore programming or distributed concurrency. Some experience with Python programming language is assumed.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed January 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789343052/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 80
    Online Resource
    Online Resource
    Python deep learning projects : 9 projects demystifying neural network and deep learning models for building intelligent systems (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789134759 , 1789134757
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Machine learning ; Neural networks (Computer science) ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Insightful practical projects to master deep learning and neural network architectures using Python, Keras and MXNet About This Book Rich projects on computer vision, NLP, and image processing Build your own neural network and explore innumerable possibilities with deep learning Explore the power of Python for deep learning in various scenarios using insightful projects Who This Book Is For This book is for developers, data scientists, or enthusiasts, who have sound knowledge of python programming, basics of machine learning, and want to break into deep learning, either for opening a new career opportunity or for realizing their own AI projects. What You Will Learn Set up a Deep Learning development environment on AWS, using GPU-powered instances and the Deep Learning AMI Implement Sequence to Sequence Networks for modeling natural language processing Develop an end-to-end speech recognition system Build a system for pixel-wise semantic labeling of an image Develop a system that generates images and their regions In Detail Deep Learning has quietly revolutionized every field of Artificial Intelligence, enabling the development of applications that a few years ago were considered almost impossible. This book will provide all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each new project will build upon the experience and knowledge accumulated in the previous ones, allowing the reader to progressively master the subject. You will learn neural network models implementing a text classifier system using Recurrent Neural network model (RNN) and optimize it to understand the shortcomings you might come across while implementing a simple deep learning system. If you are looking to implement intelligent systems like Automatic Machine Translation, Handwriting Generation, Character Text Generation, Object Classification in Photographs, Colorization of Images, Image Caption Generation, Character Text Generation or Automatic Game Playing into your application then this book is for you. By the end of this book, you will come across various Recurrent and Convolutional Neural network implementations with practical hands-on to modeling concepts like regularization, Gradient clipping, and gradient normalization, LSTM, Bidirectional RNN's through a series engaging projects. Style and approach One stop guide to gain deep learning knowledge and skills by working on authentic, engagin...
    Note: Description based on online resource; title from title page (Safari, viewed March 1, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 81
    Online Resource
    Online Resource
    Hands-on reinforcement learning with Python : master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788836913 , 178883691X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Reinforcement learning ; Electronic books ; Electronic books ; local
    Abstract: A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python About This Book Your entry point into the world of artificial intelligence using the power of Python An example-rich guide to master various RL and DRL algorithms Explore various state-of-the-art architectures along with math Who This Book Is For If you're a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book. What You Will Learn Understand the basics of reinforcement learning methods, algorithms, and elements Train an agent to walk using OpenAI Gym and Tensorflow Understand the Markov Decision Process, Bellman's optimality, and TD learning Solve multi-armed-bandit problems using various algorithms Master deep learning algorithms, such as RNN, LSTM, and CNN with applications Build intelligent agents using the DRQN algorithm to play the Doom game Teach agents to play the Lunar Lander game using DDPG Train an agent to win a car racing game using dueling DQN In Detail Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence. Style and approach This is a hands-on book designed to further expand your machine learning skills by understanding reinforcement to deep reinforcement le...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed August 2, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788836524/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 82
    Online Resource
    Online Resource
    Python GUI programming with Tkinter : develop responsive and powerful GUI applications with Tkinter (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788835688 , 1788835689
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Graphical user interfaces (Computer systems) ; Programming ; Python (Computer program language) ; Object-oriented programming (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Find out how to create visually stunning and feature-rich applications by empowering Python's built-in Tkinter GUI toolkit About This Book Explore Tkinter's powerful features to easily design and customize your GUI application Learn the basics of 2D and 3D animation in GUI applications. Learn to integrate stunning Data Visualizations using Tkinter Canvas and Matplotlib. Who This Book Is For This book will appeal to developers and programmers who would like to build GUI-based applications. Knowledge of Python is a prerequisite. What You Will Learn Implement the tools provided by Tkinter to design beautiful GUIs Discover cross-platform development through minor customizations in your existing application Visualize graphs in real time as data comes in using Tkinter's animation capabilities Use PostgreSQL authentication to ensure data security for your application Write unit tests to avoid regressions when updating code In Detail Tkinter is a lightweight, portable, and easy-to-use graphical toolkit available in the Python Standard Library, widely used to build Python GUIs due to its simplicity and availability. This book teaches you to design and build graphical user interfaces that are functional, appealing, and user-friendly using the powerful combination of Python and Tkinter. After being introduced to Tkinter, you will be guided step-by-step through the application development process. Over the course of the book, your application will evolve from a simple data-entry form to a complex data management and visualization tool while maintaining a clean and robust design. In addition to building the GUI, you'll learn how to connect to external databases and network resources, test your code to avoid errors, and maximize performance using asynchronous programming. You'll make the most of Tkinter's cross-platform availability by learning how to maintain compatibility, mimic platform-native look and feel, and build executables for deployment across popular computing platforms. By the end of this book, you will have the skills and confidence to design and build powerful high-end GUI applications to solve real-world problems. Style and approach This is a comprehensive guide that explores the essential Tkinter features and modules and implements them in building real-world cross-platform GUI applications Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://ww...
    Note: Description based on online resource; title from title page (Safari, viewed June 7, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788835886/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 83
    Online Resource
    Online Resource
    Hands-on automated machine learning : a beginner's guide to building automated machine learning systems using AutoML and Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788622288 , 1788622286
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Automate data and model pipelines for faster machine learning applications About This Book Build automated modules for different machine learning components Understand each component of a machine learning pipeline in depth Learn to use different open source AutoML and feature engineering platforms Who This Book Is For If you're a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you. You'll also find this book useful if you're an ML engineer or data professional interested in developing quick machine learning pipelines for your projects. Prior exposure to Python programming will help you get the best out of this book. What You Will Learn Understand the fundamentals of Automated Machine Learning systems Explore auto-sklearn and MLBox for AutoML tasks Automate your preprocessing methods along with feature transformation Enhance feature selection and generation using the Python stack Assemble individual components of ML into a complete AutoML framework Demystify hyperparameter tuning to optimize your ML models Dive into Machine Learning concepts such as neural networks and autoencoders Understand the information costs and trade-offs associated with AutoML In Detail AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners' work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you'll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you'll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions. Style and approach Step by step approach to understand how to automate y...
    Note: Description based on online resource; title from title page (Safari, viewed May 23, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788629898/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 84
    Online Resource
    Online Resource
    Mastering machine learning algorithms : expert techniques to implement popular machine learning algorithms and fine-tune your models (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788625906 , 1788625900
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: Explore and master the most important algorithms for solving complex machine learning problems. About This Book Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Who This Book Is For This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide. What You Will Learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques In Detail Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems...
    Note: Description based on online resource; title from title page (Safari, viewed June 29, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788621113/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 85
    Online Resource
    Online Resource
    Hands-on artificial intelligence for search : building intelligent applications and perform enterprise searches (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789612479 , 1789612470
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Artificial intelligence ; Application software ; Development ; Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Make your searches more responsive and smarter by applying Artificial Intelligence to it Key Features Enter the world of Artificial Intelligence with solid concepts and real-world use cases Make your applications intelligent using AI in your day-to-day apps and become a smart developer Design and implement artificial intelligence in searches Book Description With the emergence of big data and modern technologies, AI has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for AI in fields such as robotics, predictive analytics, finance, and more. In this book, you will understand what artificial intelligence is. It explains in detail basic search methods: Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search, which can be used to make intelligent decisions when the initial state, end state, and possible actions are known. Random solutions or greedy solutions can be found for such problems. But these are not optimal in either space or time and efficient approaches in time and space will be explored. We will also understand how to formulate a problem, which involves looking at it and identifying its initial state, goal state, and the actions that are possible in each state. We also need to understand the data structures involved while implementing these search algorithms as they form the basis of search exploration. Finally, we will look into what a heuristic is as this decides the quality of one sub-solution over another and helps you decide which step to take. What you will learn Understand the instances where searches can be used Understand the algorithms that can be used to make decisions more intelligent Formulate a problem by specifying its initial state, goal state, and actions Translate the concepts of the selected search algorithm into code Compare how basic search algorithms will perform for the application Implement algorithmic programming using code examples Who this book is for This book is for developers who are keen to get started with Artificial Intelligence and develop practical AI-based applications. Those developers who want to upgrade their normal applications to smart and intelligent versions will find this book useful. A basic knowledge and understanding of Python are assumed.
    Note: Description based on online resource; title from title page (Safari, viewed October 8, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789611151/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 86
    Online Resource
    Online Resource
    Python data science essentials : a practitioner's guide covering essential data science principles, tools, and techniques (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789531893 , 1789531896
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition.
    Keywords: Python (Computer program language) ; Database management ; Information visualization ; Electronic books ; Electronic books ; local
    Abstract: Gain useful insights from your data using popular data science tools Key Features A one-stop guide to Python libraries such as pandas and NumPy Comprehensive coverage of data science operations such as data cleaning and data manipulation Choose scalable learning algorithms for your data science tasks Book Description Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You'll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learn Set up your data science toolbox on Windows, Mac, and Linux Use the core machine learning methods offered by the scikit-learn library Manipulate, fix, and explore data to solve data science problems Learn advanced explorative and manipulative techniques to solve data operations Optimize your machine learning models for optimized performance Explore and cluster graphs, taking advantage of interconnections and links in your data Who this book is for If you're a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support an...
    Note: Description based on online resource; title from title page (viewed November 5, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789537864/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 87
    Online Resource
    Online Resource
    Machine learning with Apache Spark quick start guide : uncover patterns, derive actionable insights, and learn from big data using MLlib (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789349375 , 1789349370
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time Key Features Make a hands-on start in the fields of Big Data, Distributed Technologies and Machine Learning Learn how to design, develop and interpret the results of common Machine Learning algorithms Uncover hidden patterns in your data in order to derive real actionable insights and business value Book Description Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data. What you will learn Understand how Spark fits in the context of the big data ecosystem Understand how to deploy and configure a local development environment using Apache Spark Understand how to design supervised and unsupervised learning models Build models to perform NLP, deep learning, and cognitive services using Spark ML libraries Design real-time machine learning pipelines in Apache Spark Become familiar with advanced techniques for processing a large volume of data by applying machine learning algorithms Who this book is for This book is aimed at Business Analysts, Data ...
    Note: Description based on online resource; title from title page (viewed February 13, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789346565/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 88
    Online Resource
    Online Resource
    Mastering Python networking : your one-stop solution to using Python for network automation, DevOps, and test-driven development (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789130263 , 1789130263
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; Application software ; Development ; Computer networks ; Management ; Electronic books ; Electronic books ; local
    Abstract: Master the art of using Python for a diverse range of network engineering tasks Key Features Explore the power of Python libraries to tackle difficult network problems efficiently and effectively Use Python for network device automation, DevOps, and software-defined networking Become an expert in implementing advanced network-related tasks with Python Book Description Networks in your infrastructure set the foundation for how your application can be deployed, maintained, and serviced. Python is the ideal language for network engineers to explore tools that were previously available to systems engineers and application developers. In this second edition of Mastering Python Networking, you'll embark on a Python-based journey to transition from traditional network engineers to network developers ready for the next-generation of networks. This book begins by reviewing the basics of Python and teaches you how Python can interact with both legacy and API-enabled network devices. As you make your way through the chapters, you will then learn to leverage high-level Python packages and frameworks to perform network engineering tasks for automation, monitoring, management, and enhanced security. In the concluding chapters, you will use Jenkins for continuous network integration as well as testing tools to verify your network. By the end of this book, you will be able to perform all networking tasks with ease using Python. What you will learn Use Python libraries to interact with your network Integrate Ansible 2.5 using Python to control Cisco, Juniper, and Arista eAPI network devices Leverage existing frameworks to construct high-level APIs Learn how to build virtual networks in the AWS Cloud Understand how Jenkins can be used to automatically deploy changes in your network Use PyTest and Unittest for Test-Driven Network Development Who this book is for Mastering Python Networking is for network engineers and programmers who want to use Python for networking. Basic familiarity with Python programming and networking-related concepts such as Transmission Control Protocol/Internet Protocol (TCP/IP) will be useful. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.
    Note: Previous edition published: 2017. - Description based on online resource; title from title page (Safari, viewed September 20, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789135992/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 89
    Online Resource
    Online Resource
    Python for offensive PenTest : a practical guide to ethical hacking and penetration testing using Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788832465 , 1788832469
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Penetration testing (Computer security) ; Application software ; Testing ; Electronic books ; Electronic books ; local
    Abstract: Your one-stop guide to using Python, creating your own hacking tools, and making the most out of resources available for this programming language About This Book Comprehensive information on building a web application penetration testing framework using Python Master web application penetration testing using the multi-paradigm programming language Python Detect vulnerabilities in a system or application by writing your own Python scripts Who This Book Is For This book is for ethical hackers; penetration testers; students preparing for OSCP, OSCE, GPEN, GXPN, and CEH; information security professionals; cybersecurity consultants; system and network security administrators; and programmers who are keen on learning all about penetration testing. What You Will Learn Code your own reverse shell (TCP and HTTP) Create your own anonymous shell by interacting with Twitter, Google Forms, and SourceForge Replicate Metasploit features and build an advanced shell Hack passwords using multiple techniques (API hooking, keyloggers, and clipboard hijacking) Exfiltrate data from your target Add encryption (AES, RSA, and XOR) to your shell to learn how cryptography is being abused by malware Discover privilege escalation on Windows with practical examples Countermeasures against most attacks In Detail Python is an easy-to-learn and cross-platform programming language that has unlimited third-party libraries. Plenty of open source hacking tools are written in Python, which can be easily integrated within your script. This book is packed with step-by-step instructions and working examples to make you a skilled penetration tester. It is divided into clear bite-sized chunks, so you can learn at your own pace and focus on the areas of most interest to you. This book will teach you how to code a reverse shell and build an anonymous shell. You will also learn how to hack passwords and perform a privilege escalation on Windows with practical examples. You will set up your own virtual hacking environment in VirtualBox, which will help you run multiple operating systems for your testing environment. By the end of this book, you will have learned how to code your own scripts and mastered ethical hacking from scratch. Style and approach This book follows a practical approach that takes a gradual learning curve, building up your knowledge about ethical hacking, right from scratch. The focus is less on theory and more on practical examples through a step-by-step approach.
    Note: Description based on online resource; title from title page (Safari, viewed May 23, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788838979/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 90
    Online Resource
    Online Resource
    Machine learning for mobile : practical guide to building intelligent mobile applications powered by machine learning (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788621427 , 1788621425
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Application software ; Development ; Mobile apps ; Machine learning ; Mobile computing ; Electronic books ; Electronic books ; local
    Abstract: Leverage the power of machine learning on mobiles and build intelligent mobile applications with ease Key Features Build smart mobile applications for Android and iOS devices Use popular machine learning toolkits such as Core ML and TensorFlow Lite Explore cloud services for machine learning that can be used in mobile apps Book Description Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples. You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains. By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices. What you will learn Build intelligent machine learning models that run on Android and iOS Use machine learning toolkits such as Core ML, TensorFlow Lite, and more Learn how to use Google Mobile Vision in your mobile apps Build a spam message detection system using Linear SVM Using Core ML to implement a regression model for iOS devices Build image classification systems using TensorFlow Lite and Core ML Who this book is for If you are a mobile app developer or a machine learning enthusiast keen to use machine learning to build smart mobile applications, this book is for you. Some experience with mobile application development is all you need to get started with this book. Prior experience with machine learning will be an added bonus
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 25, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788629355/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 91
    Online Resource
    Online Resource
    Machine learning algorithms : popular algorithms for data science and machine learning (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789345483 , 1789345480
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Machine learning ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. I...
    Note: Previous edition published: 2017. - Description based on online resource; title from title page (Safari, viewed October 2, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789347999/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 92
    Online Resource
    Online Resource
    Hands-on data science with Anaconda : utilize the right mix of tools to create high-performance data science applications (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788834735 , 1788834739
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    ST 250
    Keywords: ANACONDA (Electronic resource) ; Machine learning ; Information visualization ; Electronic data processing
    Note: Description based on online resource; title from title page (Safari, viewed June 27, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788831192/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 93
    Online Resource
    Online Resource
    Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788398879 , 1788398874
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    ST 510
    Keywords: Google (Firm) ; Machine learning ; Cloud computing
    Note: Description based on online resource; title from title page (Safari, viewed May 30, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788393485/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 94
    Online Resource
    Online Resource
    Hands-on data structures and algorithms with Python : write complex and powerful using the latest features of Python 3.7 (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788991933 , 1788991931
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; Data structures (Computer science) ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: Learn to implement complex data structures and algorithms using Python Key Features Understand the analysis and design of fundamental Python data structures Explore advanced Python concepts such as Big O notation and dynamic programming Learn functional and reactive implementations of traditional data structures Book Description Data structures allow you to store and organize data efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. Hands-On Data Structures and Algorithms with Python teaches you the essential Python data structures and the most common algorithms for building easy and maintainable applications. This book helps you to understand the power of linked lists, double linked lists, and circular linked lists. You will learn to create complex data structures, such as graphs, stacks, and queues. As you make your way through the chapters, you will explore the application of binary searches and binary search trees, along with learning common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. In the concluding chapters, you will get to grips with organizing your code in a manageable, consistent, and extendable way. You will also study how to bubble sort, selection sort, insertion sort, and merge sort algorithms in detail. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. You will get insights into Python implementation of all the important and relevant algorithms. What you will learn Understand object representation, attribute binding, and data encapsulation Gain a solid understanding of Python data structures using algorithms Study algorithms using examples with pictorial representation Learn complex algorithms through easy explanation, implementing Python Build sophisticated and efficient data applications in Python Understand common programming algorithms used in Python data science Write efficient and robust code in Python 3.7 Who this book is for This book is for developers who want to learn data structures and algorithms in Python to write complex and flexible programs. Basic Python programming knowledge is expected.
    Note: Description based on online resource; title from title page (viewed February 6, 2019)
    URL: lizenzpflichtig
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 95
    Online Resource
    Online Resource
    Hands-on recommendation systems with Python : start building powerful and personalized, recommendation engines with Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788992534 , 1788992539
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Internet marketing ; Data mining ; Electronic books ; Electronic books ; local
    Abstract: With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory - you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.
    Note: Description based on online resource; title from title page (Safari, viewed August 27, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788993753/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 96
    Online Resource
    Online Resource
    Artificial intelligence by example : develop machine intelligence from scratch using real artificial intelligence use cases (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788990028 , 1788990021
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Artificial intelligence ; Data processing ; Application software ; Development ; Python (Computer program language) ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Be an adaptive thinker that leads the way to Artificial Intelligence About This Book AI-based examples to guide you in designing and implementing machine intelligence Develop your own method for future AI solutions Acquire advanced AI, machine learning, and deep learning design skills Who This Book Is For Artificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book. What You Will Learn Use adaptive thinking to solve real-life AI case studies Rise beyond being a modern-day factory code worker Acquire advanced AI, machine learning, and deep learning designing skills Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology Understand future AI solutions and adapt quickly to them Develop out-of-the-box thinking to face any challenge the market presents In Detail Artificial Intelligence has the potential to replicate humans in every field. This book serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop business vision. Style and approach An easy-to-follow step by step guide which will help you get to grips with real world application of Artificial Intelligence Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your a...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed June 27, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788990547/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 97
    Online Resource
    Online Resource
    Python advanced guide to artificial intelligence : expert machine learning systems and intelligent agents using Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789951721 , 1789951720
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Learning path
    Keywords: Python (Computer program language) ; Artificial intelligence ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and more Build, deploy, and scale end-to-end deep neural network models in a production environment Book Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe Bonaccorso Mastering TensorFlow 1.x by Armando Fandango Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn Explore how an ML model can be trained, optimized, and evaluated Work with Autoencoders and Generative Adversarial Networks Explore the most important Reinforcement Learning techniques Build end-to-end deep learning (CNN, RNN, and Autoencoders) models Who this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some un...
    Note: Description based on online resource; title from cover (Safari, viewed February 22, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789957211/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 98
    Online Resource
    Online Resource
    Hands-on reactive programming with Python : event-driven development unraveled with RxPY (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789132755 , 1789132754
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: A comprehensive guide to help you understand the principles of Reactive and asynchronous programming and its benefits Key Features Explore the advantages of Reactive programming Use concurrency and parallelism in RxPY to build powerful reactive applications Deploy and scale your reactive applications using Docker Book Description Reactive programming is central to many concurrent systems, but it's famous for its steep learning curve, which makes most developers feel like they're hitting a wall. With this book, you will get to grips with reactive programming by steadily exploring various concepts This hands-on guide gets you started with Reactive Programming (RP) in Python. You will learn abouta the principles and benefits of using RP, which can be leveraged to build powerful concurrent applications. As you progress through the chapters, you will be introduced to the paradigm of Functional and Reactive Programming (FaRP), observables and observers, and concurrency and parallelism. The book will then take you through the implementation of an audio transcoding server and introduce you to a library that helps in the writing of FaRP code. You will understand how to use third-party services and dynamically reconfigure an application. By the end of the book, you will also have learned how to deploy and scale your applications with Docker and Traefik and explore the significant potential behind the reactive streams concept, and you'll have got to grips with a comprehensive set of best practices. What you will learn Structure Python code for better readability, testing, and performance Explore the world of event-based programming Grasp the use of the most common operators in Rx Understand reactive extensions beyond simple examples Master the art of writing reusable components Deploy an application on a cloud platform with Docker and Traefik Who this book is for If you are a Python developer who wants to learn Reactive programming to build powerful concurrent and asynchronous applications, this book is for you. Basic understanding of the Python language is all you need to understand the concepts covered in this book.
    Note: Description based on online resource; title from title page (Safari, viewed December 3, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789138726/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 99
    Online Resource
    Online Resource
    Java deep learning projects : implement 10 real-world deep learning applications using Deeplearning4j and open source APIs (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788996525 , 1788996526
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Java (Computer program language) ; Application program interfaces (Computer software) ; Machine learning ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Build and deploy powerful neural network models using the latest Java deep learning libraries About This Book Understand DL with Java by implementing real-world projects Master implementations of various ANN models and build your own DL systems Develop applications using NLP, image classification, RL, and GPU processing Who This Book Is For If you are a data scientist, machine learning professional, or deep learning practitioner keen to expand your knowledge by delving into the practical aspects of deep learning with Java, then this book is what you need! Get ready to build advanced deep learning models to carry out complex numerical computations. Some basic understanding of machine learning concepts and a working knowledge of Java are required. What You Will Learn Master deep learning and neural network architectures Build real-life applications covering image classification, object detection, online trading, transfer learning, and multimedia analytics using DL4J and open-source APIs Train ML agents to learn from data using deep reinforcement learning Use factorization machines for advanced movie recommendations Train DL models on distributed GPUs for faster deep learning with Spark and DL4J Ease your learning experience through 69 FAQs In Detail Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you'll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build ...
    Note: Description based on online resource; title from title page (Safari, viewed July 31, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788997454/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
  • 100
    Online Resource
    Online Resource
    Hands-on artificial intelligence for beginners : an introduction to AI concepts, algorithms, and their implementation (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788992268 , 1788992261
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Artificial intelligence ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with ease Key Features Enter the world of AI with the help of solid concepts and real-world use cases Explore AI components to build real-world automated intelligence Become well versed with machine learning and deep learning concepts Book Description Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world. Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games. By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications. What you will learn Use TensorFlow packages to create AI systems Build feedforward, convolutional, and recurrent neural networks Implement generative models for text generation Build reinforcement learning algorithms to play games Assemble RNNs, CNNs, and decoders to create an intelligent assistant Utilize RNNs to predict stock market behavior Create and scale training pipelines and deployment architectures for AI systems Who this book is for This book is designed for beginners in AI, aspiring AI developers, as well as machine learning enthusiasts with an interest in leveraging various algorithms to build powerful AI applications.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 20, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788991063/?ar
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    MPI Ethno. Forsch.
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...