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  • English  (337)
  • Birmingham, UK : Packt Publishing  (337)
  • Cambridge : Cambridge University Press
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  • Machine learning  (169)
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  • English  (337)
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  • 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
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    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
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    Online Resource
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  • 3
    Online Resource
    Online Resource
    Getting started with CockroachDB : a guide to using a modern, cloud-native, and distributed SQL database for your data-intensive apps (2022)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781800560659
    Language: English
    Pages: 1 online resource (246 pages)
    Edition: [First edition].
    DDC: 005.74
    Keywords: Database management ; SQL (Computer program language) ; Cloud computing ; Application software Development ; Web applications ; Relational databases ; Bases de données ; Gestion ; SQL (Langage de programmation) ; Infonuagique ; Logiciels d'application ; Développement ; Applications Web ; Bases de données relationnelles ; Application software ; Development ; Cloud computing ; Database management ; Relational databases ; SQL (Computer program language) ; Web applications ; Electronic books ; Electronic books
    Abstract: Get hands-on with deploying and managing your database services to provide scalable and high-speed data access on CockroachDB. Getting Started with CockroachDB will introduce you to the inner workings of CockroachDB and help you to understand how it provides faster access to distributed data through a SQL interface. The book will also uncover how you can use the database to provide solutions where the data is highly available. Starting with CockroachDB's installation, setup, and configuration, this SQL book will familiarize you with the database architecture and database design principles. You'll then discover several options that CockroachDB provides to store multiple copies of your data to ensure fast data access. The book covers the internals of CockroachDB, how to deploy and manage it on the cloud, performance tuning to get the best out of CockroachDB, and how to scale data across continents and serve it locally. In addition to this, you'll get to grips with fault tolerance and auto-rebalancing, how indexes work, and the CockroachDB Admin UI. The book will guide you in building scalable cloud services on top of CockroachDB, covering administrative and security aspects and tips for troubleshooting, performance enhancements, and a brief guideline on migrating from traditional databases. By the end of this book, you'll have gained sufficient knowledge to manage your data on CockroachDB and interact with it from your application layer.
    Note: Includes bibliographical references
    URL: lizenzpflichtig
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    Online Resource
    MPI Ethno. Forsch.
  • 4
    Online Resource
    Online Resource
    Google Workspace user guide (2022)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781801073004
    Language: English
    Pages: 1 online resource (264 pages) , illustrations
    Edition: [First edition].
    DDC: 005.5
    Keywords: Google Apps ; Integrated software ; Cloud computing ; Logiciels intégrés ; Infonuagique ; Electronic books
    Abstract: Explore the suite of apps that enhance productivity and promote efficient collaboration in your business Key Features Set up your own project in Google Workspace and improve your ability to interact with different services Understand how a combination of options can help businesses audit their data to be highly secure Deploy Google Workspace, configure users, and migrate data using Google Workspace Book Description Google Workspace has evolved from individual Google services to a suite of apps that improve productivity and promote efficient collaboration in an enterprise organization. This book takes you through the evolution of Google Workspace, features included in each Workspace edition, and various core services, such as Cloud Identity, Gmail, and Calendar. You'll explore the functionality of each configuration, which will help you make informed decisions for your organization. Later chapters will show you how to implement security configurations that are available at different layers of Workspace and also how Workspace meets essential enterprise compliance needs. You'll gain a high-level overview of the core services available in Google Workspace, including Google Apps Script, AppSheet, and Google Cloud Platform. Finally, you'll explore the different tools Google offers when you're adopting Google Cloud and migrating your data from legacy mail servers or on-premises applications over to cloud servers. By the end of this Google Workspace book, you'll be able to successfully deploy Google Workspace, configure users, and migrate data, thereby helping with cloud adoption. What you will learn Manage and configure users in your organization's Workspace account Protect email messages from phishing attacks Explore how to restrict or allow certain Marketplace apps for your users Manage all endpoints connecting to Google Workspace Understand the differences between Marketplace apps and add-ons that access Drive data Manage devices to keep your organization's data secure Migrate to Google Workspace from existing enterprise collaboration tools Who this book is for This book is for admins as well as home users, business users, and power users looking to improve their efficiency while using Google Workspace. Basic knowledge of using Google Workspace services is assumed.
    URL: lizenzpflichtig
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  • 5
    Online Resource
    Online Resource
    Software architecture with C# 10 and .NET 6 (2022)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781803235257
    Language: English
    Pages: 1 online resource (714 pages) , illustrations
    Edition: Third edition.
    DDC: 005.13/3
    Keywords: Microsoft .NET Framework ; C # (Computer program language) ; Software architecture ; Application software Development ; Cloud computing ; Microsoft .NET Framework ; Architecture logicielle ; Logiciels d'application ; Développement ; Infonuagique ; Application software ; Development ; Cloud computing ; Software architecture ; Electronic books
    Abstract: Design scalable and high-performance enterprise applications using the latest features of C# 10 and .NET 6 Key Features Gain comprehensive software architecture knowledge and the skillset to create fully modular apps Solve scalability problems in web apps using enterprise architecture patterns Master new developments in front-end architecture and the application of AI for software architects Book Description Software architecture is the practice of implementing structures and systems that streamline the software development process and improve the quality of an app. This fully revised and expanded third edition, featuring the latest features of .NET 6 and C# 10, enables you to acquire the key skills, knowledge, and best practices required to become an effective software architect. Software Architecture with C# 10 and .NET 6, Third Edition features new chapters that describe the importance of the software architect, microservices with ASP.NET Core, and analyzing the architectural aspects of the front-end in the applications, including the new approach of .NET MAUI. It also includes a new chapter focused on providing a short introduction to artificial intelligence and machine learning using ML.NET, and updated chapters on Azure Kubernetes Service, EF Core, and Blazor. You will begin by understanding how to transform user requirements into architectural needs and exploring the differences between functional and non-functional requirements. Next, you will explore how to choose a cloud solution for your infrastructure, taking into account the factors that will help you manage a cloud-based app successfully. Finally, you will analyze and implement software design patterns that will allow you to solve common development problems. By the end of this book, you will be able to build and deliver highly scalable enterprise-ready apps that meet your business requirements. What you will learn Use proven techniques to overcome real-world architectural challenges Apply architectural approaches such as layered architecture Leverage tools such as containers to manage microservices effectively Get up to speed with Azure features for delivering global solutions Program and maintain Azure Functions using C# 10 Understand when it is best to use test-driven development (TDD) Implement microservices with ASP.NET Core in modern architectures Enrich your application with Artificial Intelligence Get the best of DevOps principles to enable CI/CD environments Who this book is for This book is for engineers and senior software developers aspiring to become architects or looking to build enterprise applications with the .NET Stack. Basic familiarity with C# and .NET is required to get the most out of this book.
    Note: Includes bibliographical references and index
    URL: lizenzpflichtig
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  • 6
    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
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  • 7
    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
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  • 8
    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
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  • 9
    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
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  • 10
    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
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  • 11
    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
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  • 12
    Online Resource
    Online Resource
    Mastering identity and access management with Microsoft Azure : empower users by managing and protecting identities and data (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789131154 , 1789131154
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Windows Azure ; Cloud computing ; Computer networks ; Security measures ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Start empowering users and protecting corporate data, while managing identities and access with Microsoft Azure in different environments Key Features Understand how to identify and manage business drivers during transitions Explore Microsoft Identity and Access Management as a Service (IDaaS) solution Over 40 playbooks to support your learning process with practical guidelines Book Description Microsoft Azure and its Identity and access management are at the heart of Microsoft's software as service products, including Office 365, Dynamics CRM, and Enterprise Mobility Management. It is crucial to master Microsoft Azure in order to be able to work with the Microsoft Cloud effectively. You'll begin by identifying the benefits of Microsoft Azure in the field of identity and access management. Working through the functionality of identity and access management as a service, you will get a full overview of the Microsoft strategy. Understanding identity synchronization will help you to provide a well-managed identity. Project scenarios and examples will enable you to understand, troubleshoot, and develop on essential authentication protocols and publishing scenarios. Finally, you will acquire a thorough understanding of Microsoft Information protection technologies. What you will learn Apply technical descriptions to your business needs and deployments Manage cloud-only, simple, and complex hybrid environments Apply correct and efficient monitoring and identity protection strategies Design and deploy custom Identity and access management solutions Build a complete identity and access management life cycle Understand authentication and application publishing mechanisms Use and understand the most crucial identity synchronization scenarios Implement a suitable information protection strategy Who this book is for This book is a perfect companion for developers, cyber security specialists, system and security engineers, IT consultants/architects, and system administrators who are looking for perfectly up?to-date hybrid and cloud-only scenarios. You should have some understanding of security solutions, Active Directory, access privileges/rights, and authentication methods. Programming knowledge is not required but can be helpful for using PowerShell or working with APIs to customize your solutions.
    Note: Previous edition published: 2016. - Description based on online resource; title from title page (Safari, viewed April 5, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789132304/?ar
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  • 13
    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
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  • 14
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    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
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  • 15
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    Oracle CX Cloud Suite : deliver a seamless and personalized customer experience with the Oracle CX Suite (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788832014 , 1788832019
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Cloud computing ; Web services ; Customer relations ; Management ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Gain a complete overview of Oracle CX Cloud Suite and its tools for functions ranging from marketing to sales and commerce to service Key Features Make optimal use of your Oracle CX Cloud Suite to improve business results Achieve improved customer insights through Oracle CX's advanced capabilities Learn how to design a CX solution architecture Book Description Oracle CX Cloud offers features and capabilities that help companies excel at sales, customer management, and much more. This book is a detailed guide to implementing cloud solutions and helping administrators of all levels thoroughly understand the platform. Oracle CX Cloud Suite begins with an introduction to high-level Oracle architecture and examines what CX offers over CRM. You'll explore the different cloud-based tools for marketing, sales, and customer services, among others. The book then delves into deployment by covering basic settings, setting up users, and provisioning. You'll see how to integrate the CX suite to work together to interact with the environment and connect with legacy systems, social connectors, and internet services. The book concludes with a use case demonstrating how the entire Oracle CX Suite is set up, and also covers how to leverage Oracle ICS and Oracle CX Cloud for hybrid deployment. By end of the book, you will have learned about the working of the Oracle CX Cloud Suite and how to orchestrate user experience across all products seamlessly. What you will learn Differentiate between Oracle CRM and CX Cloud suites Explore a variety of Oracle CX Cloud tools for marketing and sales Set up users and database connections during deployment Employ Cloud Suite CX tools to aid in planning and analysis Implement hybrid Oracle CX solutions and connect with legacy systems Integrate with social media connectors like Facebook and LinkedIn Leverage Oracle ICS and Oracle CX Suite to improve business results Who this book is for This book is for administrators who want to develop and strengthen their Oracle CX Cloud Suite skills in the areas of configuration and system management. Whether you are a new administrator or an experienced professional, this book will enhance your understanding of the new Oracle CX features.
    Note: Description based on online resource; title from title page (Safari, viewed May 14, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788834933/?ar
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  • 16
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    Hands-on G Suite for administrators : build and manage any business on top of the Google Cloud infrastructure (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789613346 , 1789613345
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Cloud computing ; Information technology ; Management ; Computing platforms ; Real-time data processing ; Electronic books ; Electronic books ; local
    Abstract: Effectively implement and administer business solutions on any scale in a cost-effective way to have a competitive advantage using Gsuite Key Features Enhance administration with Admin console and Google Apps Script Prepare for the G suite certification using the concepts in the book Learn how to use reports to monitor, troubleshoot and optimize G Suite Book Description Hands-On G Suite for Administrators is a comprehensive hands-on guide to G Suite Administration that will prepare you with all you need to know to become a certified G Suite Administrator, ready to handle all the business scales, from a small office to a large enterprise. You will start by learning the main features, tools, and services from G Suite for Business and then, you will explore all it has to offer and the best practices, so you can make the most out of it. We will explore G Suite tools in depth so you and your team get everything you need -combination of tools, settings and practices- to succeed in an intuitive, safe and collaborative way. While learning G Suite tools you will also learn how to use Google Sites and App Maker, to create from your corporate site to internal tools, live reports that seamlessly integrate with live documents, and advanced Google Services. Finally, you will learn how to set up, analyze and enforce Security, Privacy for your business and how to efficiently troubleshoot a wide variety of issues. What you will learn Setting up G Suite for the business account Work with the advanced setup of additional business domains and administrate users in multiple Explore Guite's extensive set of features to cover your team's creation and collaboration needs Setup, manage and analyze your security to prevent, find or fix any security problem in G Suite Manage Mobile devices and integrate with third-party apps Create cloud documents, working alone or collaborating in real time Who this book is for System administrators, cloud administrators, business professionals, and aspirants of G Suite admin certificate wanting to master implementing G Suite tools for various admin tasks and effectively implement the G Suite administration for business
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 14, 2019)
    URL: lizenzpflichtig
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  • 17
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    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
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  • 18
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    Serverless integration design patterns with Azure : build powerful cloud solutions that sustain next-generation products (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788390835 , 1788390830
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Microsoft Azure (Computing platform) ; Cloud computing ; Software patterns ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: A practical guide that helps you progress to using modern integration methods and leverage new cloud capability models Key Features Design critical hybrid integration solutions for your organization Gain in-depth knowledge of how to build cloud-native integration solutions Leverage cognitive services to build smart cloud solutions Book Description With more enterprises adapting cloud-based and API-based solutions, application integration has become more relevant and significant than ever before. Parallelly, Serverless Integration has gained popularity, as it helps agile organizations to build integration solutions quickly without having to worry about infrastructure costs. With Microsoft Azure's serverless offerings, such as Logic Apps, Azure Functions, API Management, Azure Event Grid and Service Bus, organizations can build powerful, secure, and scalable integration solutions with ease. The primary objective of this book is to help you to understand various serverless offerings included within Azure Integration Services, taking you through the basics and industry practices and patterns. This book starts by explaining the concepts of services such as Azure Functions, Logic Apps, and Service Bus with hands-on examples and use cases. After getting to grips with the basics, you will be introduced to API Management and building B2B solutions using Logic Apps Enterprise Integration Pack. This book will help readers to understand building hybrid integration solutions and touches upon Microsoft Cognitive Services and leveraging them in modern integration solutions. Industry practices and patterns are brought to light at appropriate opportunities while explaining various concepts. What you will learn Learn about the design principles of Microsoft Azure Serverless Integration Get insights into Azure Functions, Logic Apps, Azure Event Grid and Service Bus Secure and manage your integration endpoints using Azure API Management Build advanced B2B solutions using Logic Apps, Enterprise Integration Pack Monitor integration solutions using tools available on the market Discover design patterns for hybrid integration Who this book is for Serverless Integration Design Patterns with Azure is for you if you are a solution architect or integration professional aiming to build complex cloud solutions for your organization. Developers looking to build next-level hybrid or cloud solutions will also find this book useful. Prior programming knowledge is necessary.
    Note: Description based on online resource; title from title page (Safari, viewed March 28, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788399234/?ar
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  • 19
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    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
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  • 20
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    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
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  • 21
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    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
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  • 22
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    Azure Resource Manager templates quick start guide : create, deploy, and manage Azure resources with ARM templates using best practices (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789612271 , 1789612276
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Microsoft Azure (Computing platform) ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Compose and decompose ARM templates and use advanced concepts like looping, conditions, dependencies, PowerShell and Desired State Configuration. Key Features Design, implement, and unit test ARM templates Develop and deploy ARM templates following security best practices Book Description Azure Resource Manager (ARM) templates are declarations of Azure resources in the JSON format to provision and maintain them using infrastructure as code. This book gives practical solutions and examples for provisioning and managing various Azure services using ARM templates. The book starts with an understanding of infrastructure as code, a refresher on JSON, and then moves on to explain the fundamental concepts of ARM templates. Important concepts like iteration, conditional evaluation, security, usage of expressions, and functions will be covered in detail. You will use linked and nested templates to create modular ARM templates. You will see how to create multiple instances of the same resources, how to nest and link templates, and how to establish dependencies between them. You will also learn about implementing design patterns, secure template design, the unit testing of ARM templates, and adopting best practices. By the end of this book, you will understand the entire life cycle of ARM templates and their testing, and be able to author them for complex deployments. What you will learn Understand the foundations of ARM templates including nested and linked templates Design, create, and unit test ARM templates using best practices Learn about conditional deployments, looping, Custom Script Extensions using PowerShell, Bash, and DSC Implement design patterns related to ARM templates Run post-deployment PowerShell and Desired State Configuration scripts Create solutions and deploy them on Azure using ARM templates Who this book is for This books is for developers, DevOps engineers, and architects who have experience in Azure.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed April 22, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789803235/?ar
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  • 23
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    Intelligent automation with VMware : apply machine learning techniques to VMware virtualization and networking (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789806793 , 1789806798
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: VMware ; Virtual computer systems ; Cloud computing ; Computer networks ; Management ; Electronic books ; Electronic books ; local
    Abstract: Use self-driven data centers to reduce management complexity by deploying Infrastructure as Code to gain value from investments. Key Features Add smart capabilities in VMware Workspace ONE to deliver customer insights and improve overall security Optimize your HPC and big data infrastructure with the help of machine learning Automate your VMware data center operations with machine learning Book Description This book presents an introductory perspective on how machine learning plays an important role in a VMware environment. It offers a basic understanding of how to leverage machine learning primitives, along with a deeper look into integration with the VMware tools used for automation today. This book begins by highlighting how VMware addresses business issues related to its workforce, customers, and partners with emerging technologies such as machine learning to create new, intelligence-driven, end user experiences. You will learn how to apply machine learning techniques incorporated in VMware solutions for data center operations. You will go through management toolsets with a focus on machine learning techniques. At the end of the book, you will learn how the new vSphere Scale-Out edition can be used to ensure that HPC, big data performance, and other requirements can be met (either through development or by fine-tuning guidelines) with mainstream products. What you will learn Orchestrate on-demand deployments based on defined policies Automate away common problems and make life easier by reducing errors Deliver services to end users rather than to virtual machines Reduce rework in a multi-layered scalable manner in any cloud Explore the centralized life cycle management of hybrid clouds Use common code so you can run it across any cloud Who this book is for This book is intended for those planning, designing, and implementing the virtualization/cloud components of the Software-Defined Data Center foundational infrastructure. It helps users to put intelligence in their automation tasks to get self driving data center. It is assumed that the reader has knowledge of, and some familiarity with, virtualization concepts and related topics, including storage, security, and networking.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789802160/?ar
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  • 24
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    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
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  • 25
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    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
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    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)
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    Implementing AWS : design, build, and manage your infrastructure : leverage AWS features to build highly secure, fault-tolerant, and scalable cloud environments (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788831062 , 1788831063
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Learning path
    Keywords: Amazon Web Services (Firm) ; Cloud computing ; Computer software ; Development ; Web services ; Electronic books ; Electronic books ; local
    Abstract: Work through exciting recipes to administer your AWS cloud Key Features Build secure environments using AWS components and services Explore core AWS features with real-world applications and best practices Design and build Lambda functions using real-world examples Book Description With this Learning Path, you'll explore techniques to easily manage applications on the AWS cloud. You'll begin with an introduction to serverless computing, its advantages, and the fundamentals of AWS. The following chapters will guide you on how to manage multiple accounts by setting up consolidated billing, enhancing your application delivery skills, with the latest AWS services such as CodeCommit, CodeDeploy, and CodePipeline to provide continuous delivery and deployment, while also securing and monitoring your environment's workflow. It'll also add to your understanding of the services AWS Lambda provides to developers. To refine your skills further, it demonstrates how to design, write, test, monitor, and troubleshoot Lambda functions. By the end of this Learning Path, you'll be able to create a highly secure, fault-tolerant, and scalable environment for your applications. This Learning Path includes content from the following Packt products: AWS Administration: The Definitive Guide, Second Edition by Yohan Wadia AWS Administration Cookbook by Rowan Udell, Lucas Chan Mastering AWS Lambda by Yohan Wadia, Udita Gupta What you will learn Explore the benefits of serverless computing and applications Deploy apps with AWS Elastic Beanstalk and Amazon Elastic File System Secure environments with AWS CloudTrail, AWSConfig, and AWS Shield Run big data analytics with Amazon EMR and Amazon Redshift Back up and safeguard data using AWS Data Pipeline Create monitoring and alerting dashboards using CloudWatch Effectively monitor and troubleshoot serverless applications with AWS Design serverless apps via AWS Lambda, DynamoDB, and API Gateway Who this book is for This Learning Path is specifically designed for IT system and network administrators, AWS architects, and DevOps engineers who want to effectively implement AWS in their organization and easily manage daily activities. Familiarity with Linux, web services, cloud computing platforms, virtualization, networking, and other administration-related tasks will assist in understanding the concepts in the book. Prior hands-on experience with AWS core services such as EC2, IAM, S3, and programming languages, such as Node.Js, ...
    Note: Description based on online resource; title from title page (Safari, viewed March 19, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788835770/?ar
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  • 28
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    Building Google Cloud Platform solutions : develop scalable applications from scratch and make them globally available in almost any language (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781838648701 , 1838648704
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Series Statement: Learning path
    Keywords: Cloud computing ; Application software ; Development ; Computing platforms ; Electronic books ; Electronic books ; local
    Abstract: Build cost-effective and robust cloud solutions with Google Cloud Platform (GCP) using these simple and practical recipes Key Features Explore the various service offerings of the GCP Host a Python application on Google Compute Engine Securely maintain application states with Cloud Storage, Datastore, and Bigtable Book Description GCP is a cloud computing platform with a wide range of products and services that enable you to build and deploy cloud-hosted applications. This Learning Path will guide you in using GCP and designing, deploying, and managing applications on Google Cloud. You will get started by learning how to use App Engine to access Google's scalable hosting and build software that runs on this framework. With the help of Google Compute Engine, you'll be able to host your workload on virtual machine instances. The later chapters will help you to explore ways to implement authentication and security, Cloud APIs, and command-line and deployment management. As you hone your skills, you'll understand how to integrate your new applications with various data solutions on GCP, including Cloud SQL, Bigtable, and Cloud Storage. Following this, the book will teach you how to streamline your workflow with tools, including Source Repositories, Container Builder, and Stackdriver. You'll also understand how to deploy and debug services with IntelliJ, implement continuous delivery pipelines, and configure robust monitoring and alerts for your production systems. By the end of this Learning Path, you'll be well versed with GCP's development tools and be able to develop, deploy, and manage highly scalable and reliable applications. This Learning Path includes content from the following Packt products: Google Cloud Platform for Developers Ted Hunter and Steven Porter Google Cloud Platform Cookbook by Legorie Rajan PS What you will learn Host an application using Google Cloud Functions Migrate a MySQL database to Cloud Spanner Configure a network for a highly available application on GCP Learn simple image processing using Storage and Cloud Functions Automate security checks using Policy Scanner Deploy and run services on App Engine and Container Engine Minimize downtime and mitigate issues with Stackdriver Monitoring and Debugger Integrate with big data solutions, including BigQuery, Dataflow, and Pub/Sub Who this book is for This Learning Path is for IT professionals, engineers, and developers who want to implement Google Cloud in their organizati...
    Note: Description based on online resource; title from title page (Safari, viewed May 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781838647438/?ar
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  • 29
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    Migrating applications to the cloud with Azure : re-architect and rebuild your applications using cloud-native technologies (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781839214035 , 1839214031 , 9781839217470
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Windows Azure ; Cloud computing ; Windows Azure ; Cloud computing ; Electronic books
    Note: Description based on online resource; title from title page (Safari, viewed June 3, 2020)
    URL: lizenzpflichtig
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  • 30
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    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
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    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
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    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
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    Hands-on RESTful web API design patterns and best practices : design, develop, and deploy highly adaptable, scalable, and secure RESTful web APIs (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788998581 , 1788998588
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Application program interfaces (Computer software) ; Web applications ; Computer networks ; Management ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Build effective RESTful APIs for enterprise with design patterns and REST framework's out-of-the-box capabilities Key Features Understand advanced topics such as API gateways, API securities, and cloud Implement patterns programmatically with easy-to-follow examples Modernize legacy codebase using API connectors, layers, and microservices Book Description This book deals with the Representational State Transfer (REST) paradigm, which is an architectural style that allows networked devices to communicate with each other over the internet. With the help of this book, you'll explore the concepts of service-oriented architecture (SOA), event-driven architecture (EDA), and resource-oriented architecture (ROA). This book covers why there is an insistence for high-quality APIs toward enterprise integration. It also covers how to optimize and explore endpoints for microservices with API gateways and touches upon integrated platforms and Hubs for RESTful APIs. You'll also understand how application delivery and deployments can be simplified and streamlined in the REST world. The book will help you dig deeper into the distinct contributions of RESTful services for IoT analytics and applications. Besides detailing the API design and development aspects, this book will assist you in designing and developing production-ready, testable, sustainable, and enterprise-grade APIs. By the end of the book, you'll be empowered with all that you need to create highly flexible APIs for next-generation RESTful services and applications. What you will learn Explore RESTful concepts, including URI, HATEOAS, and Code on Demand Study core patterns like Statelessness, Pagination, and Discoverability Optimize endpoints for linked microservices with API gateways Delve into API authentication, authorization, and API security implementations Work with Service Orchestration to craft composite and process-aware services Expose RESTful protocol-based APIs for cloud computing Who this book is for This book is primarily for web, mobile, and cloud services developers, architects, and consultants who want to build well-designed APIs for creating and sustaining enterprise-class applications. You'll also benefit from this book if you want to understand the finer details of RESTful APIs and their design techniques along with some tricks and tips.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 22, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788992664/?ar
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    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
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    Azure for architects : implementing cloud design, DevOps, containers, IoT, and serverless solutions on your public cloud (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789611649 , 1789611644
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Windows Azure ; Cloud computing ; Architecture ; Data processing ; Electronic books ; Electronic books ; local
    Abstract: Create advanced data and integrated solutions using Azure Event Grid, functions, and containers Key Features Get familiar with the different design patterns available in Microsoft Azure Develop Azure cloud architecture and a pipeline management system Get to know the security best practices for your Azure deployment Book Description Over the years, Azure cloud services have grown quickly, and the number of organizations adopting Azure for their cloud services is also gradually increasing. Leading industry giants are finding that Azure fulfills their extensive cloud requirements. Azure for Architects ? Second Edition starts with an extensive introduction to major designing and architectural aspects available with Azure. These design patterns focus on different aspects of the cloud, such as high availability, security, and scalability. Gradually, we move on to other aspects, such as ARM template modular design and deployments. This is the age of microservices and serverless is the preferred implementation mechanism for them. This book covers the entire serverless stack available in Azure including Azure Event Grid, Azure Functions, and Azure Logic Apps. New and advance features like durable functions are discussed at length. A complete integration solution using these serverless technologies is also part of the book. A complete chapter discusses all possible options related to containers in Azure including Azure Kubernetes services, Azure Container Instances and Registry, and Web App for Containers. Data management and integration is an integral part of this book that discusses options for implementing OLTP solutions using Azure SQL, Big Data solutions using Azure Data factory and Data Lake Storage, eventing solutions using stream analytics, and Event Hubs. This book will provide insights into Azure governance features such as tagging, RBAC, cost management, and policies. By the end of this book, you will be able to develop a full-?edged Azure cloud solution that is Enterprise class and future-ready. What you will learn Create an end-to-end integration solution using Azure Serverless Stack Learn Big Data solutions and OLTP?based applications on Azure Understand DevOps implementations using Azure DevOps Architect solutions comprised of multiple resources in Azure Develop modular ARM templates Develop Governance on Azure using locks, RBAC, policies, tags and cost Learn ways to build data solutions on Azure Understand the various options related to...
    Note: Previous edition published: 2017. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 25, 2019)
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    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
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    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)
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    Hands-on IoT solutions with Blockchain : discover how converging IoT and Blockchain can help you build effective solutions (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789133264 , 1789133262
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Internet of things ; Blockchains (Databases) ; Cloud computing ; Embedded Internet devices ; Electronic books ; Electronic books ; local
    Abstract: Integrate an end-to-end logistic chain using IBM Blockchain and IoT platforms Key Features Explore practical implementation of ledger technology in the IoT architecture Study security best practices for your smart devices Understand Blockchain implementation for end-to-end IoT solutions Book Description Blockchain has been the hot topic of late thanks to cryptocurrencies. To make matters more interesting, the financial market is looking for ways to reduce operational costs and generate new business models, and this is where blockchain solutions come into the picture. In addition to this, with Internet of Things (IoT) trending and Arduino, Raspberry Pi, and other devices flooding the market, you can now create cheap devices even at home. Hands-On IoT Solutions with Blockchain starts with an overview of IoT concepts in the current business scenario. It then helps you develop your own device on the IBM Watson IoT platform and create your fi rst IoT solution using Watson and Intel Edison.Once you are familiar with IoT, you will learn about Blockchain technology and its use cases. You will also work with the Hyperledger framework and develop your own Blockchain network. As you progress through the chapters, you'll work with problem statements and learn how to design your solution architecture so that you can create your own integrated Blockchain and IoT solution. The next set of chapters will explain how to implement end-to-end Blockchain solutions with IoT using the IBM Cloud platform. By the end of this book, you will have mastered the convergence of IoT and Blockchain technology and exploited the best practices and drivers to develop a bulletproof integrated solution. What you will learn Understand the key roles of IoT in the current market Study the different aspects of IBM Watson IoT platform Create devices, gateways, and applications connected to the platform Explore the fundamentals of Blockchain Define good use cases for Blockchain Discover the Hyperledger Fabric and Composer frameworks Develop an IBM Watson IoT application using a Intel Edison Integrate IoT with the Blockchain platform Who this book is for Hands-On IoT Solutions with Blockchain is for you if you are an Internet of Things (IoT) analyst, architect, engineer, or any stakeholder responsible for security mechanisms on an IoT infrastructure. This book is also for IT professionals who want to start developing solutions using Blockchain and IoT on the IBM Cloud platform. Basic und...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed March 4, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789132243/?ar
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    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
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  • 40
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    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
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  • 41
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    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
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  • 42
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    Hands-on Kubernetes on Azure : run your applications securely and at scale on the most widely adopted orchestration platform (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789535235 , 1789535239
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Windows Azure ; Application software ; Development ; Cloud computing ; Web applications ; Electronic books ; Electronic books ; local
    Abstract: Efficiently deploy and manage Kubernetes clusters on a cloud Key Features Deploy highly scalable applications with Kubernetes on Azure Leverage AKS to deploy, manage, and operations of Kubernetes Gain best practices from this guide to increase efficiency of container orchestration service on Cloud Book Description Microsoft is now one of the most significant contributors to Kubernetes open source projects. Kubernetes helps to create, configure, and manage a cluster of virtual machines that are preconfigured to run containerized applications. This book will be your resource for achieving successful container orchestration and deployment of Kubernetes clusters on Azure. You will learn how to deploy and manage highly scalable applications, along with how to set up a production-ready Kubernetes cluster on Azure. With this book, you will be able to reduce the complexity and operational overheads of managing a Kubernetes cluster on Azure. By the end of this book, you will not only be capable of deploying and managing Kubernetes clusters on Azure with ease, but also have the knowledge of industry best practices to work with advanced Azure Kubernetes Services (AKS) concepts for complex systems. What you will learn Get to grips with Microsoft AKS deployment, management, and operations Learn about the benefits of using Microsoft AKS, as well as the limitations, and avoid potential problems Integrate Microsoft toolchains such as Visual Studio Code, and Git Implement simple and advanced AKS solutions Implement the automated scalability and high reliability of secure deployments with Microsoft AKS Use kubectl commands to monitor applications Who this book is for If you're a cloud engineer, cloud solution provider, sysadmin, site reliability engineer, or a developer interested in DevOps and are looking for an extensive guide to running Kubernetes in the Azure environment then, this book is for you. Though any previous knowledge of Kubernetes is not expected, some experience with Linux and Docker containers would be beneficial.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 14, 2019)
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    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
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  • 44
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    Azure networking cookbook : practical recipes to manage network traffic in Azure, optimize performance, and secure Azure resources (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789807707 , 1789807700
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Windows Azure ; Cloud computing ; Computer networks ; Management ; Electronic books ; Electronic books ; local
    Abstract: Learn to distribute network traffic, optimize application delivery, and defend network-level threats. Key Features Quickly get up and running with Azure networking solutions Level-up your cloud networking skills by planning, implementing, configuring, and securing your infrastructure network with Azure Leverage Azure networking services to provide applications highly available and fault tolerant environment Book Description Microsoft provides organizations with an effective way of managing their network with Azure's networking services. No matter the size of your organization, Azure provides a way to highly reliable performance and secure connectivity with its networking services. The book starts with an introduction to the Azure networking like creating Azure virtual networks, designing address spaces and subnets. Then you will learn to create and manage network security groups, application security groups, and IP addresses in Azure. Gradually, we move on to various aspects like S2S, P2S, and Vnet2Vnet connections, DNS and routing, load balancers and traffic manager. This book will cover every aspect and function required to deliver practical recipes to help readers learn from basic cloud networking practices to planning, implementing, and securing their infrastructure network with Azure. Readers will not only be able to upscale their current environment but will also learn to monitor, diagnose, and ensure secure connectivity. After learning to deliver a robust environment readers will also gain meaningful insights from recipes on best practices. By the end of this book, readers will gain hands-on experience in providing cost-effective solutions that benefit organizations. What you will learn Learn to create Azure networking services Understand how to create and work on hybrid connections Configure and manage Azure network services Learn ways to design high availability network solutions in Azure Discover how to monitor and troubleshoot Azure network resources Learn different methods of connecting local networks to Azure virtual networks Who this book is for This book is targeted towards cloud architects, cloud solution providers, or any stakeholders dealing with networking on the Azure cloud. Some prior understanding of Microsoft Azure will be a plus point.
    Note: Description based on online resource; title from title page (Safari, viewed May 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789800227/?ar
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  • 45
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    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
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  • 46
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    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
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  • 47
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    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
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    Apache Spark 2 data processing and real-time analytics : master complex big data processing, stream analytics, and machine learning with Apache (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789959918 , 1789959918
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Spark (Electronic resource : Apache Software Foundation) ; Data mining ; Big data ; Machine learning ; Electronic data processing ; Electronic books ; Electronic books ; local
    Abstract: Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key Features Master the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.x and Scala Book Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: Mastering Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn Get to grips with all the features of Apache Spark 2.x Perform highly optimized real-time big data processing Use ML and DL techniques with Spark MLlib and third-party tools Analyze structured and unstructured data using SparkSQL and GraphX Understand tuning, debugging, and monitoring of big data applications Build scalable and fault-tolerant streaming applications Develop scalable recommendation engines Who this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.
    Note: Description based on online resource; title from title page (Safari, viewed March 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789959208/?ar
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    Numerical computing with Python : harness the power of Python to analyze and find hidden patterns in the data (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789957228 , 1789957222
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Series Statement: Learning path
    Keywords: Python (Computer program language) ; Data mining ; Information visualization ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Understand, explore, and effectively present data using the powerful data visualization techniques of Python Key Features Use the power of Pandas and Matplotlib to easily solve data mining issues Understand the basics of statistics to build powerful predictive data models Grasp data mining concepts with helpful use-cases and examples Book Description Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: Statistics for Machine Learning by Pratap Dangeti Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim Pandas Cookbook by Theodore Petrou What you will learn Understand the statistical fundamentals to build data models Split data into independent groups Apply aggregations and transformations to each group Create impressive data visualizations Prepare your data and design models Clean up data to ease data analysis and visualization Create insightful visualizations with Matplotlib and Seaborn Customize the model to suit your own predictive goals Who this book is for If you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed February 20, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789953633/?ar
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  • 50
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    Learning Microsoft Cognitive Services : use Cognitive Services APIs to add AI capabilities to your applications (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789803686 , 1789803683
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Third edition.
    Keywords: Microsoft Visual studio ; Application program interfaces (Computer software) ; Artificial intelligence ; Machine learning ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Build smarter applications with AI capabilities using Microsoft Cognitive Services APIs without much hassle Key Features Explore the Cognitive Services APIs for building machine learning applications Build applications with computer vision, speech recognition, and language processing capabilities Learn to implement human-like cognitive intelligence for your applications Book Description Microsoft Cognitive Services is a set of APIs for adding intelligence to your application and leverage the power of AI to solve any business problem using the cognitive capabilities. This book will be your practical guide to working with cognitive APIs developed by Microsoft and provided with the Azure platform to developers and businesses. You will learn to integrate the APIs with your applications in Visual Studio. The book introduces you to about 24 APIs including Emotion, Language, Vision, Speech, Knowledge, and Search among others. With the easy-to-follow examples you will be able to develop applications for image processing, speech recognition, text procession, and so on to enhance the capability of your applications to perform more human-like tasks. Going ahead, the book will help you work with the datasets that enable your applications to process various data in form of image, videos, and texts. By the end of the book, you will get confident to explore the Cognitive Services APIs for your applications and make them intelligent for deploying in businesses. What you will learn Identify a person through visual and audio inspection Reduce user effort by utilizing AI-like capabilities Understand how to analyze images and texts in different ways Analyze images using Vision APIs Add video analysis to applications using Vision APIs Utilize Search to find anything you want Analyze text to extract information and explore text structure Who this book is for Learning Microsoft Cognitive Services is for developers and machine learning enthusiasts who want to get started with building intelligent applications without much programming knowledge. Some prior knowledge of .NET and Visual Studio will help you undertake the tasks explained in this book.
    Note: Includes index. - Description based on online resource; title from cover (viewed November 6, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789800616/?ar
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    Azure serverless computing cookbook : build and monitor Azure applications hosted on serverless architecture using Azure functions (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789617047 , 1789617049
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Microsoft Azure (Computing platform) ; Cloud computing ; Application program interfaces (Computer software) ; Electronic books ; Electronic books ; local
    Abstract: Over 50 practical recipes that will help you develop and deliver high-quality and reliable cloud-centric Azure serverless applications for your organization Key Features Leverage practical use cases to build a robust serverless environment Enhance Azure Functions with continuous deployment using Visual Studio Team Services Deploy and manage cost-effective and highly available serverless applications using Azure Functions Book Description Microsoft provides a solution for easily running small segments of code in the cloud with Azure Functions. The second edition of Azure Serverless Computing Cookbook starts with intermediate-level recipes on serverless computing along with some use cases demonstrating the benefits and key features of Azure Functions. You'll explore the core aspects of Azure Functions, such as the services it provides, how you can develop and write Azure Functions, and how to monitor and troubleshoot them. As you make your way through the chapters, you'll get practical recipes on integrating DevOps with Azure Functions, and providing continuous integration and continuous deployment with Azure DevOps. This book also provides hands-on, step-by-step tutorials based on real-world serverless use cases to guide you through configuring and setting up your serverless environments with ease. You will also learn how to build solutions for complex, real-world, workflow-based scenarios quickly and with minimal code using Durable Functions. In the concluding chapters, you will ensure enterprise-level security within your serverless environment. The most common tips and tricks that you need to be aware of when working with Azure Functions on production environments will also be covered in this book. By the end of this book, you will have all the skills required for working with serverless code architecture, providing continuous delivery to your users. What you will learn Integrate Azure Functions with other Azure services Understand cloud application development using Azure Functions Employ durable functions for developing reliable and durable serverless applications Use SendGrid and Twilio services Explore code reusability and refactoring in Azure Functions Configure serverless applications in a production environment Who this book is for If you are a cloud administrator, architect, or developer who wants to build scalable systems and deploy serverless applications with Azure Functions, then the Azure Serverless Computing Cookbook is for you...
    Note: Previous edition published: 2017. - Description based on online resource; title from title page (Safari, viewed February 4, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789615265/?ar
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    Deep learning with TensorFlow : explore neural networks and build intelligent systems with Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788831833 , 1788831837
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition, fully revised and updated.
    Keywords: Machine learning ; Artificial intelligence ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow. About This Book Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Gain real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn Apply deep machine intelligence and GPU computing with TensorFlow Access public datasets and use TensorFlow to load, process, and transform the data Discover how to use the high-level TensorFlow API to build more powerful applications Use deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications In Detail Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects. Style and approach This step-by-step guide explores common, and not so common, deep neural networks, and shows ho...
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed May 8, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788831109/?ar
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    Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788624534 , 178862453X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Artificial intelligence ; Machine learning ; Python (Computer program language) ; Neural networks (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results Key Features Explore the most advanced deep learning techniques that drive modern AI results Implement Deep Neural Networks, Autoencoders, GANs, VAEs, and Deep Reinforcement Learning A wide study of GANs, including Improved GANs, Cross-Domain GANs and Disentangled Representation GANs Book Description Recent developments in deep learning, including GANs, Variational Autoencoders, and Deep Reinforcement Learning, are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You'll learn how to implement deep learning models with Keras and Tensorflow, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create Autoencoders. You then learn all about Generative Adversarial Networks (GANs), and how they can open new levels of AI performance. Variational AutoEncoders (VAEs) are implemented, and you'll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement Deep Reinforcement Learning (DRL) such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Cutting-edge techniques in human-like AI performance Implement advanced deep learning models using Keras The building blocks for advanced techniques - MLPs, CNNs, and RNNs Deep neural networks ? ResNet and DenseNet Autoencoders and Variational AutoEncoders (VAEs) Generative Adversarial Networks (GANs) and creative AI techniques Disentangled Representation GANs, and Cross-Domain GANs Deep Reinforcement Learning (DRL) meth...
    Note: "Expert insight.". - Includes bibliographical references and index. - Description based on online resource; title from cover (Safari, viewed December 10, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788629416/?ar
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    Hands-on deep learning for images with TensorFlow : build intelligent computer vision applications using TensorFlow and Keras (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789532517 , 1789532515
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Computer vision ; Artificial intelligence ; Image processing ; Electronic books ; Electronic books ; local
    Abstract: Explore TensorFlow's capabilities to perform efficient deep learning on images Key Features Discover image processing for machine vision Build an effective image classification system using the power of CNNs Leverage TensorFlow's capabilities to perform efficient deep learning Book Description TensorFlow is Google's popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow's capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras. What you will learn Build machine learning models particularly focused on the MNIST digits Work with Docker and Keras to build an image classifier Understand natural language models to process text and images Prepare your dataset for machine learning Create classical, convolutional, and deep neural networks Create a RESTful image classification server Who this book is for Hands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.
    Note: Description based on online resource; title from title page (Safari, viewed September 5, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789538670/?ar
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    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
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    Hands-on ensemble learning with R : a beginner's guide to combining the power of machine learning algorithms using ensemble techniques (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788629171 , 1788629175
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Machine learning ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: Explore powerful R packages to create predictive models using ensemble methods Key Features Implement machine learning algorithms to build ensemble-efficient models Explore powerful R packages to create predictive models using ensemble methods Learn to build ensemble models on large datasets using a practical approach Book Description Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques ? bagging, random forest, and boosting ? then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples. What you will learn Carry out an essential review of re-sampling methods, bootstrap, and jackknife Explore the key ensemble methods: bagging, random forests, and boosting Use multiple algorithms to make strong predictive models Enjoy a comprehensive treatment of boosting methods Supplement methods with statistical tests, such as ROC Walk through data structures in classification, regression, survival, and time series data Use the supplied R code to implement ensemble methods Learn stacking method to combine heterogeneous machine learning models Who this book is for This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.
    Note: Includes bibliographical references and index. - Description based on online resource; title from cover (Safari, viewed August 21, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788624145/?ar
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    AWS administration : the definitive guide : design, build, and manage your infrastructure on Amazon Web Services (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788477178 , 1788477170
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Amazon Web Services (Firm) ; Web services ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Leverage this step-by-step guide to build a highly secure, fault-tolerant, and scalable Cloud environment About This Book Learn how to leverage various Amazon Web Services (AWS) components and services to build a secure, reliable, and robust environment to host your applications on. Delve into core AWS service offerings with hands-on tutorials, real-world use case scenarios, and best practices. A self-paced, systematic, and step-by-step guide to learning and implementing AWS in your own environment. Who This Book Is For This book is for those who want to learn and leverage the rich plethora of services provided by AWS. Although no prior experience with AWS is required, it is recommended that you have some hands-on experience of Linux, Web Services, and basic networking. What You Will Learn Take an in-depth look at what's new with AWS, along with how to effectively manage and automate your EC2 infrastructure with AWS Systems Manager Deploy and scale your applications with ease using AWS Elastic Beanstalk and Amazon Elastic File System Secure and govern your environments using AWS CloudTrail, AWS Config, and AWS Shield Learn the DevOps way using a combination of AWS CodeCommit, AWS CodeDeploy, and AWS CodePipeline Run big data analytics and workloads using Amazon EMR and Amazon Redshift Learn to back up and safeguard your data using AWS Data Pipeline Get started with the Internet of Things using AWS IoT and AWS Greengrass In Detail Many businesses are moving from traditional data centers to AWS because of its reliability, vast service offerings, lower costs, and high rate of innovation. AWS can be used to accomplish a variety of both simple and tedious tasks. Whether you are a seasoned system admin or a rookie, this book will help you to learn all the skills you need to work with the AWS cloud. This book guides you through some of the most popular AWS services, such as EC2, Elastic Beanstalk, EFS, CloudTrail, Redshift, EMR, Data Pipeline, and IoT using a simple, real-world, application-hosting example. This book will also enhance your application delivery skills with the latest AWS services, such as CodeCommit, CodeDeploy, and CodePipeline, to provide continuous delivery and deployment, while also securing and monitoring your environment's workflow. Each chapter is designed to provide you with maximal information about each AWS service, coupled with easy to follow, hands-on steps, best practices, tips, and recommendations. By the end of the book...
    Note: Previous edition published: 2016. - Description based on online resource; title from title page (Safari, viewed April 25, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788478793/?ar
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    Professional Azure SQL database administration : equip yourself with the skills you need to manage and maintain your SQL databases on the Microsoft cloud (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789535679 , 1789535670
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: SQL server ; Windows Azure ; Database management ; Client/server computing ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Discover how you can migrate a traditional on-premise SQL server database to a cloud-based solution with Microsoft Azure. Built with database administrators in mind, this book emulates different scenarios you might come across while working with large, complex SQL database migrations and provides solutions for effectively managing the migrated databases. Key Features Implement backup, restore, and recovery of Azure SQL databases Create shards and elastic pools to scale Azure SQL databases Automate common management tasks with PowerShell Implement over 40 practical activities and exercises across 24 topics to reinforce your learning Book Description As the cloud version of SQL Server, Azure SQL Database differs in key ways when it comes to management, maintenance, and administration. It's important to know how to administer SQL Database to fully benefit from all of the features and functionality that it provides. This book addresses important aspects of an Azure SQL Database instance such as migration, backup restorations, pricing policies, security, scalability, monitoring, performance optimization, high availability, and disaster recovery. It is a complete guide for database administrators, and ideal for those who are planning to migrate from on premise SQL Server database to an Azure SQL Server database. What you will learn Learn how to provision a new database or migrate an existing on-premise solution Understand how to backup, restore, secure, and scale your own Azure SQL Database Optimize the performance by monitoring and tuning your cloud-based SQL instance Implement high availability and disaster recovery procedures with SQL Database Develop a roadmap for your own scalable cloud solution with Azure SQL Database Who this book is for This book is ideal for database administrators, database developers, or application developers who are interested in developing or migrating existing applications with Azure SQL Database. Prior experience of working with an on-premise SQL Server deployment and brief knowledge of PowerShell and C# are recommended prerequisites.
    Note: Includes index. - Description based on online resource; title from cover (Safari, viewed August 28, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789538854/?ar
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    Learn Unity ML-Agents : fundamentals of Unity machine learning : incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789131864 , 1789131863
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Unity (Electronic resource) ; Computer games ; Programming ; Machine learning ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity About This Book Learn how to apply core machine learning concepts to your games with Unity Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your games Learn How to build multiple asynchronous agents and run them in a training scenario Who This Book Is For This book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity. The reader will be required to have a working knowledge of C# and a basic understanding of Python. What You Will Learn Develop Reinforcement and Deep Reinforcement Learning for games. Understand complex and advanced concepts of reinforcement learning and neural networks Explore various training strategies for cooperative and competitive agent development Adapt the basic script components of Academy, Agent, and Brain to be used with Q Learning. Enhance the Q Learning model with improved training strategies such as Greedy-Epsilon exploration Implement a simple NN with Keras and use it as an external brain in Unity Understand how to add LTSM blocks to an existing DQN Build multiple asynchronous agents and run them in a training scenario In Detail Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem. Style and approach This book focuses on the foundations of ML, RL and DL for building agents in a game or simulation
    Note: Description based on online resource; title from title page (Safari, viewed July 31, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789138139/?ar
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    Hands-on meta learning with Python : meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789537024 , 1789537029
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Python (Computer program language) ; Machine learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworks Key Features Understand the foundations of meta learning algorithms Explore practical examples to explore various one-shot learning algorithms with its applications in TensorFlow Master state of the art meta learning algorithms like MAML, reptile, meta SGD Book Description Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning. By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models. What you will learn Understand the basics of meta learning methods, algorithms, and types Build voice and face recognition models using a siamese network Learn the prototypical network along with its variants Build relation networks and matching networks from scratch Implement MAML and Reptile algorithms from scratch in Python Work through imitation learning and adversarial meta learning Explore task agnostic meta learning and deep meta learning Who this book is for Hands-On Meta Learning with Python is for machine learning enthusiasts, AI researchers, and data scientists who want to explore meta learning as an advanced approach for training machine learning models. Working knowledge of machine learning concepts and Python programming is necessary.
    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/-/9781789534207/?ar
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    Learning AWS : design, build, and deploy responsive applications using AWS cloud components (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787289314 , 1787289311
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Amazon Web Services (Firm) ; File organization (Computer science) ; Computer programs ; Web applications ; Development ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Discover techniques and tools for building serverless applications with AWS About This Book Get well-versed with building and deploying serverless APIs with microservices Learn to build distributed applications and microservices with AWS Step Functions A step-by-step guide that will get you up and running with building and managing applications on the AWS platform Who This Book Is For If you are an I.T. professional or a system architect who wants to improve infrastructure using AWS, then this book is for you. It is also for programmers who are new to AWS and want to build highly efficient, scalable applications. What You Will Learn Set up your AWS account and get started with the basic concepts of AWS Learn about AWS terminology and identity access management Acquaint yourself with important elements of the cloud with features such as computing, ELB, and VPC Back up your database and ensure high availability by having an understanding of database-related services in the AWS cloud Integrate AWS services with your application to meet and exceed non-functional requirements Create and automate infrastructure to design cost-effective, highly available applications In Detail Amazon Web Services (AWS) is the most popular and widely-used cloud platform. Administering and deploying application on AWS makes the applications resilient and robust. The main focus of the book is to cover the basic concepts of cloud-based development followed by running solutions in AWS Cloud, which will help the solutions run at scale. This book not only guides you through the trade-offs and ideas behind efficient cloud applications, but is a comprehensive guide to getting the most out of AWS. In the first section, you will begin by looking at the key concepts of AWS, setting up your AWS account, and operating it. This guide also covers cloud service models, which will help you build highly scalable and secure applications on the AWS platform. We will then dive deep into concepts of cloud computing with S3 storage, RDS and EC2. Next, this book will walk you through VPC, building realtime serverless environments, and deploying serverless APIs with microservices. Finally, this book will teach you to monitor your applications, and automate your infrastructure and deploy with CloudFormation. By the end of this book, you will be well-versed with the various services that AWS provides and will be able to leverage AWS infrastructure to accelerate the development process. Style an...
    Note: Description based on online resource; title from title page (Safari, viewed March 6, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781787281066/?ar
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    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
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    Hands-on microservices : monitoring and testing : a performance engineer's guide to the continuous testing and monitoring of microservices (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789138405 , 178913840X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Application software ; Development ; Mobile apps ; Development ; Web applications ; Development ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Learn and implement various techniques related to testing, monitoring and optimization for microservices architecture. Key Features Learn different approaches for testing microservices to design and implement, robust and secure applications Become more efficient while working with microservices Explore Testing and Monitoring tools such as JMeter, Ready API,and AppDynamics Book Description Microservices are the latest "right" way of developing web applications. Microservices architecture has been gaining momentum over the past few years, but once you've started down the microservices path, you need to test and optimize the services. This book focuses on exploring various testing, monitoring, and optimization techniques for microservices. The book starts with the evolution of software architecture style, from monolithic to virtualized, to microservices architecture. Then you will explore methods to deploy microservices and various implementation patterns. With the help of a real-world example, you will understand how external APIs help product developers to focus on core competencies. After that, you will learn testing techniques, such as Unit Testing, Integration Testing, Functional Testing, and Load Testing. Next, you will explore performance testing tools, such as JMeter, and Gatling. Then, we deep dive into monitoring techniques and learn performance benchmarking of the various architectural components. For this, you will explore monitoring tools such as Appdynamics, Dynatrace, AWS CloudWatch, and Nagios. Finally, you will learn to identify, address, and report various performance issues related to microservices. What you will learn Understand the architecture of microservices and how to build services Establish how external APIs help to accelerate the development process Understand testing techniques, such as unit testing, integration testing, end-to-end testing, and UI/functional testing Explore various tools related to the performance testing, monitoring, and optimization of microservices Design strategies for performance testing Identify performance issues and fine-tune performance Who this book is for This book is for developers who are involved with microservices architecture to develop robust and secure applications. Basic knowledge of microservices is essential in order to get the most out of this book.
    Note: Description based on online resource; title from title page (viewed January 7, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789133608/?ar
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    TensorFlow machine learning projects : build 13 real-world projects with advanced numerical computations using the Python ecosystem (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789132403 , 1789132401
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Artificial intelligence ; Machine learning ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects Key Features Use machine learning and deep learning principles to build real-world projects Get to grips with TensorFlow's impressive range of module offerings Implement projects on GANs, reinforcement learning, and capsule network Book Description TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits - simplicity, efficiency, and flexibility - of using TensorFlow in various real-world projects. With the help of this book, you'll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you'll get to grips with using TensorFlow for machine learning projects; you'll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you'll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You'll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you'll have gained the required expertise to build full-fledged machine learning projects at work. What you will learn Understand the TensorFlow ecosystem using various datasets and techniques Create recommendation systems for quality product recommendations Build projects using CNNs, NLP, and Bayesian neural networks Play Pac-Man using deep reinforcement learning Deploy scalable TensorFlow-based machine learning systems Generate your own book script using RNNs Who this book is for TensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using ...
    Note: Description based on online resource; title from title page (viewed February 5, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789132212/?ar
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    Kubernetes for developers : use Kubernetes to develop, test, and deploy your applications with the help of containers (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788830607 , 1788830601
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Application software ; Development ; Cloud computing ; Computer software ; Development ; Software architecture ; Electronic books ; Electronic books ; local
    Abstract: A developer's field-guide to designing scalable services using Kubernetes About This Book Develop and run your software using containers within a Kubernetes environment Get hands-on experience of using Kubernetes with DevOps concepts such as continuous integration, benchmark testing, monitoring, and so on Pragmatic example-based approach showing how to use Kubernetes in the development process Who This Book Is For If you are a full-stack or back-end software developers interested, curious, or being asked to test as well as run the code you're creating, you can leverage Kubernetes to make that process simpler and consistent regardless of where you deploy. If you're looking for developer focused examples in NodeJS and Python for how to build, test, deploy, and run your code with Kubernetes, this is perfect for you. What You Will Learn Build your software into containers Deploy and debug software running in containers within Kubernetes Declare and add configuration through Kubernetes Define how your application fits together, using internal and external services Add feedback to your code to help Kubernetes manage your services Monitor and measure your services through integration testing and in production deployments In Detail Kubernetes is documented and typically approached from the perspective of someone running software that has already been built. Kubernetes may also be used to enhance the development process, enabling more consistent testing and analysis of code to help developers verify not only its correctness, but also its efficiency. This book introduces key Kubernetes concepts, coupled with examples of how to deploy and use them with a bit of Node.js and Python example code, so that you can quickly replicate and use that knowledge. You will begin by setting up Kubernetes to help you develop and package your code. We walk you through the setup and installation process before working with Kubernetes in the development environment. We then delve into concepts such as automating your build process, autonomic computing, debugging, and integration testing. This book covers all the concepts required for a developer to work with Kubernetes. By the end of this book, you will be in a position to use Kubernetes in development ecosystems. Style and approach This book will cover examples using NodeJS and Python that walk you through building containers, defining your deployments, deploying, debugging, testing, and generally interacting with your co...
    Note: Description based on online resource; title from title page (Safari, viewed May 8, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788834759/?ar
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    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
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    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
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    OpenCV 3.x with Python by example : make the most of OpenCV and Python to build applications for object recognition and augmented reality (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788396769 , 1788396766
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; OpenCV (Computer program language) ; Computer vision ; Application software ; Development ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Learn the techniques for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications using examples on different functions of OpenCV. About This Book Learn how to apply complex visual effects to images with OpenCV 3.x and Python Extract features from an image and use them to develop advanced applications Build algorithms to help you understand image content and perform visual searches Get to grips with advanced techniques in OpenCV such as machine learning, artificial neural network, 3D reconstruction, and augmented reality Who This Book Is For This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV and Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on. What You Will Learn Detect shapes and edges from images and videos How to apply filters on images and videos Use different techniques to manipulate and improve images Extract and manipulate particular parts of images and videos Track objects or colors from videos Recognize specific object or faces from images and videos How to create Augmented Reality applications Apply artificial neural networks and machine learning to improve object recognition In Detail Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular Ope...
    Note: Previous edition published: 2015. - Description based on online resource; title from title page (Safari, viewed February 21, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788396905/?ar
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    Serverless design patterns and best practices : build, secure, and deploy enterprise ready serverless applications with AWS to improve developer productivity (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788624381 , 1788624386
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Amazon Web Services (Firm) ; Software patterns ; Application software ; Development ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Get started with designing your serverless application using optimum design patterns and industry standard practices About This Book Learn the details of popular software patterns and how they are applied to serverless applications Understand key concepts and components in serverless designs Walk away with a thorough understanding of architecting serverless applications Who This Book Is For If you're a software architect, engineer, or someone who wants to build serverless applications, which are non-trivial in complexity and scope, then this book is for you. Basic knowledge of programming and serverless computing concepts are assumed. What You Will Learn Comprehend the popular design patterns currently being used with serverless architectures Understand the various design options and corresponding implementations for serverless web application APIs Learn multiple patterns for data-intensive serverless systems and pipelines, including MapReduce and Lambda Architecture Learn how to leverage hosted databases, queues, streams, storage services, and notification services Understand error handling and system monitoring in a serverless architecture a serverless architecture Learn how to set up a serverless application for continuous integration, continuous delivery, and continuous deployment In Detail Serverless applications handle many problems that developers face when running systems and servers. The serverless pay-per-invocation model can also result in drastic cost savings, contributing to its popularity. While it's simple to create a basic serverless application, it's critical to structure your software correctly to ensure it continues to succeed as it grows. Serverless Design Patterns and Best Practices presents patterns that can be adapted to run in a serverless environment. You will learn how to develop applications that are scalable, fault tolerant, and well-tested. The book begins with an introduction to the different design pattern categories available for serverless applications. You will learn the trade-offs between GraphQL and REST and how they fare regarding overall application design in a serverless ecosystem. The book will also show you how to migrate an existing API to a serverless backend using AWS API Gateway. You will learn how to build event-driven applications using queuing and streaming systems, such as AWS Simple Queuing Service (SQS) and AWS Kinesis. Patterns for data-intensive serverless application are also explained, inc...
    Note: Description based on online resource; title from title page (Safari, viewed May 15, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788620642/?ar
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    Cloud-native applications in Java : build microservice-based cloud-native applications that dynamically scale (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787128842 , 1787128849
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Java (Computer program language) ; Cloud computing ; Web applications ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Highly available microservice-based web apps for Cloud with Java About This Book Take advantage of the simplicity of Spring to build a full-fledged application Let your applications run faster while generating smaller cloud service bills Integrate your application with various tools such as Docker and ElasticSearch and use specific tools in Azure and AWS Who This Book Is For Java developers who want to build secure, resilient, robust and scalable applications that are targeted for cloud based deployment, will find this book helpful. Some knowledge of Java, Spring, web programming and public cloud providers (AWS, Azure) should be sufficient to get you through the book. What You Will Learn See the benefits of the cloud environment when it comes to variability, provisioning, and tooling support Understand the architecture patterns and considerations when developing on the cloud Find out how to perform cloud-native techniques/patterns for request routing, RESTful service creation, Event Sourcing, and more Create Docker containers for microservices and set up continuous integration using Jenkins Monitor and troubleshoot an application deployed in the cloud environment Explore tools such as Docker and Kubernetes for containerization and the ELK stack for log aggregation and visualization Use AWS and Azure specific tools to design, develop, deploy, and manage applications Migrate from monolithic architectures to a cloud native deployment In Detail Businesses today are evolving so rapidly that they are resorting to the elasticity of the cloud to provide a platform to build and deploy their highly scalable applications. This means developers now are faced with the challenge of building build applications that are native to the cloud. For this, they need to be aware of the environment, tools, and resources they're coding against. If you're a Java developer who wants to build secure, resilient, robust, and scalable applications that are targeted for cloud-based deployment, this is the book for you. It will be your one stop guide to building cloud-native applications in Java Spring that are hosted in On-prem or cloud providers - AWS and Azure The book begins by explaining the driving factors for cloud adoption and shows you how cloud deployment is different from regular application deployment on a standard data centre. You will learn about design patterns specific to applications running in the cloud and find out how you can build a microservice in Java S...
    Note: Description based on online resource; title from title page (Safari, viewed March 16, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781787124349/?ar
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    Mastering machine learning for penetration testing : develop an extensive skill set to break self-learning systems using Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788993111 , 178899311X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Penetration testing (Computer security) ; Machine learning ; Python (Computer program language) ; Computer networks Security measures
    Note: Description based on online resource; title from title page (Safari, viewed July 24, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788997409/?ar
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    R deep learning projects : master the techniques to design and develop neural network models in R (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788474559 , 1788474554
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Machine learning ; R (Computer program language) ; Data mining
    Note: Description based on online resource; title from title page (viewed March 14, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788478403/?ar
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    Cloud analytics with Google Cloud Platform : an end-to-end guide to processing and analyzing big data using Google Cloud Platform (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788838597 , 1788838599
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Google (Firm) ; Cloud computing ; Big data ; Computing platforms ; Real-time data processing
    Note: Description based on online resource; title from title page (Safari, viewed June 1, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788839686/?ar
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    Extending OpenStack : leverage extended OpenStack projects to implement containerization, deployment, and architecting robust cloud solutions (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781786466136 , 1786466139
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Cloud computing ; Information technology Management ; Open source software
    Note: Description based on online resource; title from title page (viewed March 30, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781786465535/?ar
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    VMware cross-cloud architecture : automate and orchestrate your software-defined data center on AWS (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787288584 , 1787288587
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Amazon Web Services (Firm) ; VMware ; Virtual computer systems ; Cloud computing
    Note: Description based on online resource; title from title page (Safari, viewed May 2, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781787283435/?ar
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    Ansible 2 cloud automation cookbook : write Ansible playbooks for AWS, Google Cloud, Microsoft Azure, and OpenStack (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788298773 , 1788298772
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Amazon Web Services (Firm) ; Windows Azure ; Cloud computing ; Information technology Management ; Application software Development
    Note: Description based on online resource; title from title page (Safari, viewed March 20, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: lizenzpflichtig
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    Mastering Spring Boot 2.0 : build modern, cloud-native, and distributed systems using Spring Boot (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787125148 , 1787125149
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    RVK:
    ST 250
    Keywords: Spring (Software framework) ; Application software Development ; Cloud computing
    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/-/9781787127562/?ar
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    Google Cloud Platform cookbook : implement, deploy, maintain, and migrate applications on Google Cloud Platform (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788294560 , 1788294564
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Cloud computing ; Computing platforms ; Real-time data processing ; Application software Development
    Note: Description based on online resource; title from title page (Safari, viewed May 15, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788291996/?ar
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    Amazon Web Services bootcamp : develop a scalable, reliable, and highly available cloud environment with AWS (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788291811 , 1788291816
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Amazon Web Services (Firm) ; Web services ; Cloud computing
    Note: Description based on online resource; title from title page (Safari, viewed May 3, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788294454/?ar
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    Puppet 5 cookbook : jump start your Puppet 5.x deployment using engaging and practical recipes (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788627504 , 1788627504
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Fourth edition.
    Keywords: Puppet (Computer file) ; Client/server computing ; Database management ; Cloud computing ; High performance computing ; Electronic books ; Electronic books ; local
    Abstract: Write custom plugins for Puppet, including facts, providers, and functions About This Book Grasp recipes that work with centralized and decentralized deployments Explore language differences and enhancements anticipated in Puppet version 5.x Gain expert understanding of Puppet's latest and most advanced features Who This Book Is For Puppet 5 Cookbook is for anyone who builds and administers servers, especially in a web operations context. You'll need some experience of Linux systems administration, including familiarity with the command line, filesystem, and text editing. No prior programming experience is required. What You Will Learn Discover the latest and most advanced features of Puppet Bootstrap your Puppet installation using powerful tools like Rake Master techniques to deal with centralized and decentralized Puppet deployments Use exported resources and forge modules to set up Puppet modules Create efficient manifests to streamline your deployments Automate Puppet master deployment using Git hooks and PuppetDB Make Puppet reliable, performant, and scalable In Detail Puppet is a configuration management system that automates all your IT configurations, giving you control of managing each node.Puppet 5 Cookbook will take you through Puppet's latest and most advanced features, including Docker containers, Hiera, and AWS Cloud Orchestration. Updated with the latest advancements and best practices, this book delves into various aspects of writing good Puppet code, which includes using Puppet community style, checking your manifests with puppet-lint, and learning community best practices with an emphasis on real-world implementation. You will learn to set up, install, and create your first manifests with Puppet version control, and also understand various sysadmin tasks, including managing config files, using Augeas, and generating files from snippets and templates. As the book progresses, you'll explore virtual resources and use Puppet's resource scheduling and auditing features. In the concluding chapters, you'll walk through managing applications and writing your own resource types, providers, and external node classifiers. By the end of this book, you will have learned to report, log, and debug your system. Style and approach A recipe-based guide filled with quick step-by-step instructions that are immediately applicable
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed August 1, 2018)
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    Hands-on machine learning for algorithmic trading : design and implement investment strategies based on smart algorithms that learn from data using Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789342710 , 1789342716
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Python (Computer program language) ; Finance ; Data processing ; Finance ; Statistical methods ; Electronic books ; Electronic books ; local
    Abstract: Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is for Hands-On Ma...
    Note: Description based on online resource; title from title page (Safari, viewed February 25, 2019)
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    OpenCV 3 computer vision with Python cookbook : leverage the power of OpenCV 3 and Python to build computer vision applications (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788478755 , 1788478754
    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: Recipe-based approach to tackle the most common problems in Computer Vision by leveraging the functionality of OpenCV using Python APIs About This Book Build computer vision applications with OpenCV functionality via Python API Get to grips with image processing, multiple view geometry, and machine learning Learn to use deep learning models for image classification, object detection, and face recognition Who This Book Is For This book is for developers who have a basic knowledge of Python. If you are aware of the basics of OpenCV and are ready to build computer vision systems that are smarter, faster, more complex, and more practical than the competition, then this book is for you. What You Will Learn Get familiar with low-level image processing methods See the common linear algebra tools needed in computer vision Work with different camera models and epipolar geometry Find out how to detect interesting points in images and compare them Binarize images and mask out regions of interest Detect objects and track them in videos In Detail OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems by providing a number of recipes that you can use to improve your applications. In this book, you will learn how to process an image by manipulating pixels and analyze an image using histograms. Then, we'll show you how to apply image filters to enhance image content and exploit the image geometry in order to relay different views of a pictured scene. We'll explore techniques to achieve camera calibration and perform a multiple-view analysis. Later, you'll work on reconstructing a 3D scene from images, converting low-level pixel information to high-level concepts for applications such as object detection and recognition. You'll also discover how to process video from files or cameras and how to detect and track moving objects. Finally, you'll get acquainted with recent approaches in deep learning and neural networks. By the end of the book, you'll be able to apply your skills in OpenCV to create computer vision applications in various domains. Style and approach This book helps you learn the core concepts of OpenCV faster by taking a recipe-based approach where you can try o...
    Note: Description based on online resource; title from title page (Safari, viewed April 19, 2018)
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    Keras 2.x projects : 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789534160 , 178953416X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Reinforcement learning ; Neural networks (Computer science) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x Key Features Experimental projects showcasing the implementation of high-performance deep learning models with Keras. Use-cases across reinforcement learning, natural language processing, GANs and computer vision. Build strong fundamentals of Keras in the area of deep learning and artificial intelligence. Book Description Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems. What you will learn Apply regression methods to your data and understand how the regression algorithm works Understand the basic concepts of classification methods and how to implement them in the Keras environment Import and organize data for neural network classification analysis Learn about the role of rectified linear units in the Keras network architecture Implement a recurrent neural network to classify the sentiment of sentences from movie reviews Set the embedding layer and the tensor sizes of a network Who this book is for If you are a data scientist, machine learning engineer, deep learning practitioner or an AI engineer who wants to build speedy intelligent applications with minimal lines of codes, then this book is the best fit for you. Sound knowledge of machine learning and basic familiarity with Keras library would be useful.
    Note: Description based on online resource; title from title page (Safari, viewed February 26, 2019)
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    Mastering TensorFlow 1.x : advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788297004 , 1788297008
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Machine learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Build, scale, and deploy deep neural network models using the star libraries in Python About This Book Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes Who This Book Is For This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book. What You Will Learn Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow Scale and deploy production models with distributed and high-performance computing on GPU and clusters Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters In Detail TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs. This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images. You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow ...
    Note: Description based on online resource; title from title page (Safari, viewed February 16, 2018)
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    Go machine learning projects : eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788995191 , 1788995198
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Go (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Work through exciting projects to explore the capabilities of Go and Machine Learning Key Features Explore ML tasks and Go's machine learning ecosystem Implement clustering, regression, classification, and neural networks with Go Get to grips with libraries such as Gorgonia, Gonum, and GoCv for training models in Go Book Description Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This book will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured. The book begins by guiding you through setting up your machine learning environment with Go libraries and capabilities. You will then plunge into regression analysis of a real-life house pricing dataset and build a classification model in Go to classify emails as spam or ham. Using Gonum, Gorgonia, and STL, you will explore time series analysis along with decomposition and clean up your personal Twitter timeline by clustering tweets. In addition to this, you will learn how to recognize handwriting using neural networks and convolutional neural networks. Lastly, you'll learn how to choose the most appropriate machine learning algorithms to use for your projects with the help of a facial detection project. By the end of this book, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound understanding of the practical implementations of machine learning algorithms in real-world projects. What you will learn Set up a machine learning environment with Go libraries Use Gonum to perform regression and classification Explore time series models and decompose trends with Go libraries Clean up your Twitter timeline by clustering tweets Learn to use external services for your machine learning needs Recognize handwriting using neural networks and CNN with Gorgonia Implement facial recognition using GoCV and OpenCV Who this book is for If you're a machine learning engineer, data science professional, or Go programmer who wants to implement machine learning in your real-world projects and make smarter applications easily, this book is for you. Some coding experience in Golang and knowledge of basic machine learning concepts will help you in understanding the concepts covered in this book.
    Note: Description based on online resource; title from title page (viewed February 5, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781788993401/?ar
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    Building serverless Python web services with Zappa : build and deploy serverless applications on AWS using Zappa (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788837934 , 1788837932
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Amazon Web Services (Firm) ; Application software ; Development ; Cloud computing ; Application program interfaces (Computer software) ; Python (Computer program language) ; Electronic books ; Electronic books ; local
    Abstract: Master serverless architectures in Python and their implementation, with Zappa on three different frameworks. Key Features Scalable serverless Python web services using Django, Flask, and Pyramid. Learn Asynchronous task execution on AWS Lambda and scheduling using Zappa. Implementing Zappa in a Docker container. Book Description Serverless applications are becoming very popular these days, not just because they save developers the trouble of managing the servers, but also because they provide several other benefits such as cutting heavy costs and improving the overall performance of the application. This book will help you build serverless applications in a quick and efficient way. We begin with an introduction to AWS and the API gateway, the environment for serverless development, and Zappa. We then look at building, testing, and deploying apps in AWS with three different frameworks--Flask, Django, and Pyramid. Setting up a custom domain along with SSL certificates and configuring them with Zappa is also covered. A few advanced Zappa settings are also covered along with securing Zappa with AWS VPC. By the end of the book you will have mastered using three frameworks to build robust and cost-efficient serverless apps in Python. What you will learn Build, test, and deploy a simple web service using AWS CLI Integrate Flask-based Python applications, via AWS CLI configuration Design Rest APIs integrated with Zappa for Flask and Django Create a project in the Pyramid framework and configure it with Zappa Generate SSL Certificates using Amazon Certificate Manager Configure custom domains with AWS Route 53 Create a Docker container similar to AWS Lambda Who this book is for Python Developers who are interested in learning how to develop fast and highly scalable serverless applications in Python, will find this book useful
    Note: Description based on online resource; title from title page (Safari, viewed August 30, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788837613/?ar
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    Hands-on serverless computing : build, run, and orchestrate serverless applications using AWS Lambda, Microsoft Azure Functions, and Google Cloud Functions (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788834957 , 178883495X
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Amazon Web Services (Firm) ; Windows Azure ; Cloud computing ; Application software ; Development ; Web services ; Client/server computing ; Electronic books ; Electronic books ; local
    Abstract: Deploy functions efficiently using different cloud-based serverless offerings Key Features Understand the concept of Function-as-a-Service Implement Serverless solutions using AWS Lambda, Azure Functions and Google Cloud Functions Practical approach towards choosing the best tool for your serverless environment Book Description Serverless applications and architectures are gaining momentum and are increasingly being used by companies of all sizes. Serverless software takes care of many problems that developers face when running systems and servers, such as fault tolerance, centralized logging, horizontal scalability, and deployments. You will learn how to harness serverless technology to rapidly reduce production time and minimize your costs, while still having the freedom to customize your code, without hindering functionality. Upon finishing the book, you will have the knowledge and resources to build your own serverless application hosted in AWS, Microsoft Azure, or Google Cloud Platform, and will have experienced the benefits of event-driven technology for yourself. This hands-on guide dives into the basis of serverless architectures and how to build them using Node.js as a programming language, Visual Studio Code for code editing, and Postman for quickly and securely developing applications without the hassle of configuring and maintaining infrastructure on three public cloud platforms. What you will learn Understand the benefts of serverless computing and know when to use it Develop serverless applications on AWS, Azure, and Google Cloud Get to grips with Function as a Service (FaaS) Apply triggers to serverless functions Build event-driven apps using serverless frameworks Use the Node.js programming language to build serverless apps Use code editors, such as Visual Studio Code, as development environments Master the best development practices for creating scalable and practical solutions Who this book is for This book is targeted towards developers, system administrators or any stakeholder working in the Serverless environment and want to understand how functions work. Basic idea of serverless architecture can be an added advantage
    Note: Description based on online resource; title from title page (Safari, viewed September 19, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788836654/?ar
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    Intelligent mobile projects with TensorFlow : build 10+ artificial intelligence apps using TensorFlow Mobile and Lite for iOS, Android, and Raspberry Pi (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788628808 , 1788628802
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Mobile apps ; Application software ; Development ; Artificial intelligence ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Create Deep Learning and Reinforcement Learning apps for multiple platforms with TensorFlow About This Book Build TensorFlow-powered AI applications for mobile and embedded devices Learn modern AI topics such as computer vision, NLP, and deep reinforcement learning Get practical insights and exclusive working code not available in the TensorFlow documentation Who This Book Is For If you're an iOS/Android developer interested in building and retraining others' TensorFlow models and running them in your mobile apps, or if you're a TensorFlow developer and want to run your new and amazing TensorFlow models on mobile devices, this book is for you. You'll also benefit from this book if you're interested in TensorFlow Lite, Core ML, or TensorFlow on Raspberry Pi. What You Will Learn Classify images with transfer learning Detect objects and their locations Transform pictures with amazing art styles Understand simple speech commands Describe images in natural language Recognize drawing with Convolutional Neural Network and Long Short-Term Memory Predict stock price with Recurrent Neural Network in TensorFlow and Keras Generate and enhance images with generative adversarial networks Build AlphaZero-like mobile game app in TensorFlow and Keras Use TensorFlow Lite and Core ML on mobile Develop TensorFlow apps on Raspberry Pi that can move, see, listen, speak, and learn In Detail As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what's trending currently. So, what's better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google's TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You'll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You'll learn how to quickly build such apps with step-by-step tutorials and how to avoid many pitfalls in the process with lots of hard-earned troubleshooting tips. Style and a...
    Note: Description based on online resource; title from title page (Safari, viewed June 15, 2018)
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    Cloud native development patterns and best practices : practical architectural patterns for building modern, distributed cloud-native systems (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788476690 , 1788476697
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Cloud computing ; Application program interfaces (Computer software) ; Application software ; Development ; Electronic books ; Electronic books ; local
    Abstract: Learn to apply cloud-native patterns and practices to deliver responsive, resilient, elastic, and message-driven systems with confidence About This Book Understand the architectural patterns involved in cloud-native architectures Minimize risk by evolving your monolithic applications into distributed cloud-native systems Discover best practices for applying cloud-native patterns to your enterprise-level cloud applications Who This Book Is For This book is for developers who would like to progress into building cloud-native systems and are keen to learn the patterns involved. Basic knowledge of programming and cloud computing is required. What You Will Learn Enable massive scaling by turning your database inside out Unleash flexibility via event streaming Leverage polyglot persistence and cloud-native databases Embrace modern continuous delivery and testing techniques Minimize risk by evolving your monoliths to cloud-native Apply cloud-native patterns and solve major architectural problems in cloud environment In Detail Build systems that leverage the benefits of the cloud and applications faster than ever before with cloud-native development. This book focuses on architectural patterns for building highly scalable cloud-native systems. You will learn how the combination of cloud, reactive principles, devops, and automation enable teams to continuously deliver innovation with confidence. Begin by learning the core concepts that make these systems unique. You will explore foundational patterns that turn your database inside out to achieve massive scalability with cloud-native databases. You will also learn how to continuously deliver production code with confidence by shifting deployment and testing all the way to the left and implementing continuous observability in production. There's more-you will also learn how to strangle your monolith and design an evolving cloud-native system. By the end of the book, you will have the ability to create modern cloud-native systems. Style and approach This book follows a pragmatic approach to understand cloud-native design patterns and explains the functioning and design considerations to build modern cloud-native systems in depth. 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 fil...
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    Hands-on microservices with Kotlin : build reactive and cloud-native microservices with Kotlin using Spring 5 and Spring Boot 2.0 (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788473491 , 1788473493
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Application software ; Development ; Mobile apps ; Development ; Web applications ; Development ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Build smart, efficient, and fast enterprise-grade web implementation of the microservices architecture that can be easily scaled. About This Book Write easy-to-maintain lean and clean code with Kotlin for developing better microservices Scale your Microserivces in your own cloud with Docker and Docker Swarm Explore Spring 5 functional reactive web programming with Spring WebFlux Who This Book Is For If you are a Kotlin developer with a basic knowledge of microservice architectures and now want to effectively implement these services on enterprise-level web applications, then this book is for you What You Will Learn Understand microservice architectures and principles Build microservices in Kotlin using Spring Boot 2.0 and Spring Framework 5.0 Create reactive microservices that perform non-blocking operations with Spring WebFlux Use Spring Data to get data reactively from MongoDB Test effectively with JUnit and Kotlin Create cloud-native microservices with Spring Cloud Build and publish Docker images of your microservices Scaling microservices with Docker Swarm Monitor microservices with JMX Deploy microservices in OpenShift Online In Detail With Google's inclusion of first-class support for Kotlin in their Android ecosystem, Kotlin's future as a mainstream language is assured. Microservices help design scalable, easy-to-maintain web applications; Kotlin allows us to take advantage of modern idioms to simplify our development and create high-quality services. With 100% interoperability with the JVM, Kotlin makes working with existing Java code easier. Well-known Java systems such as Spring, Jackson, and Reactor have included Kotlin modules to exploit its language features. This book guides the reader in designing and implementing services, and producing production-ready, testable, lean code that's shorter and simpler than a traditional Java implementation. Reap the benefits of using the reactive paradigm and take advantage of non-blocking techniques to take your services to the next level in terms of industry standards. You will consume NoSQL databases reactively to allow you to create high-throughput microservices. Create cloud-native microservices that can run on a wide range of cloud providers, and monitor them. You will create Docker containers for your microservices and scale them. Finally, you will deploy your microservices in OpenShift Online. Style and approach This book guides the reader in designing and implementing services, achievin...
    Note: Description based on online resource; title from title page (viewed February 21, 2018)
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    Hybrid cloud for architects : build robust hybrid cloud solutions using AWS and OpenStack (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788627986 , 1788627989
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Amazon Web Services (Firm) ; OpenStack (Electronic resource) ; Cloud computing ; Web services
    Note: Description based on online resource; title from title page (Safari, viewed March 13, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788623513/?ar
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    Serverless web applications with React and Firebase : develop real-time applications for web and mobile platforms (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788478601 , 1788478606
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Web applications ; Application software Development ; Web services ; Cloud computing ; Mobile computing
    Note: Description based on online resource; title from title page (Safari, viewed June 1, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788477413/?ar
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    Google Cloud AI Services quick start guide : build intelligent applications with Google Cloud AI Services (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788996532 , 1788996534
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Google (Firm) ; Cloud computing ; Artificial intelligence ; Application software Development ; Application program interfaces (Computer software)
    Note: Description based on online resource; title from title page (Safari, viewed June 20, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788626613/?ar
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    Machine learning solutions : expert techniques to tackle complex machine learning problems using Python (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788398893 , 1788398890
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Python (Computer program language) ; Machine learning
    Note: Description based on online resource; title from cover (Safari, viewed June 5, 2018). - Includes index
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788390040/?ar
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  • 95
    Online Resource
    Online Resource
    Hybrid cloud for developers : develop and deploy cost-effective applications on the AWS and OpenStack platforms with ease (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788833844 , 1788833848
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Amazon Web Services (Firm) ; Cloud computing ; Application software Development ; Web services
    Note: Description based on online resource; title from title page (Safari, viewed May 16, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781788830874/?ar
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  • 96
    Online Resource
    Online Resource
    Hands-on cloud development with WildFly : develop, deploy, and configure cloud-based, enterprise Java applications with WildFly Swarm and OpenShift (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787283800 , 1787283801
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Cloud computing ; Application software Development ; Java (Computer program language)
    Note: Description based on online resource; title from title page (Safari, viewed May 2, 2018)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://learning.oreilly.com/library/view/-/9781786462374/?ar
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  • 97
    Online Resource
    Online Resource
    Implementing Oracle API Platform cloud service : design, deploy, and manage your APIs in Oracle's new API Platform (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788473446 , 1788473442
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Parallel Title: Erscheint auch als
    Keywords: Oracle (Computer file) ; Application program interfaces (Computer software) Management ; Cloud computing
    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/-/9781788478656/?ar
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  • 98
    Online Resource
    Online Resource
    Google cloud platform for architects : design and manage powerful cloud solutions (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788833073 , 1788833074
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Cloud computing ; Application software ; Development ; Web applications ; Electronic data processing ; Distributed processing ; Electronic books ; Electronic books ; local
    Abstract: Get acquainted with GCP and manage robust, highly available, and dynamic solutions to drive business objective About This Book Identify the strengths, weaknesses and ideal use-cases for individual services offered on the Google Cloud Platform Make intelligent choices about which cloud technology works best for your use-case Leverage Google Cloud Platform to analyze and optimize technical and business processes Who This Book Is For If you are a Cloud architect who is responsible to design and manage robust cloud solutions with Google Cloud Platform, then this book is for you. System engineers and Enterprise architects will also find this book useful. A basic understanding of distributed applications would be helpful, although not strictly necessary. Some working experience on other public cloud platforms would help too. What You Will Learn Set up GCP account and utilize GCP services using the cloud shell, web console, and client APIs Harness the power of App Engine, Compute Engine, Containers on the Kubernetes Engine, and Cloud Functions Pick the right managed service for your data needs, choosing intelligently between Datastore, BigTable, and BigQuery Migrate existing Hadoop, Spark, and Pig workloads with minimal disruption to your existing data infrastructure, by using Dataproc intelligently Derive insights about the health, performance, and availability of cloud-powered applications with the help of monitoring, logging, and diagnostic tools in Stackdriver In Detail Using a public cloud platform was considered risky a decade ago, and unconventional even just a few years ago. Today, however, use of the public cloud is completely mainstream - the norm, rather than the exception. Several leading technology firms, including Google, have built sophisticated cloud platforms, and are locked in a fierce competition for market share. The main goal of this book is to enable you to get the best out of the GCP, and to use it with confidence and competence. You will learn why cloud architectures take the forms that they do, and this will help you become a skilled high-level cloud architect. You will also learn how individual cloud services are configured and used, so that you are never intimidated at having to build it yourself. You will also learn the right way and the right situation in which to use the important GCP services. By the end of this book, you will be able to make the most out of Google Cloud Platform design. Style and approach A clear, conc...
    Note: Description based on online resource; title from title page (Safari, viewed July 24, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788834308/?ar
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  • 99
    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
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  • 100
    Online Resource
    Online Resource
    Computer vision projects with OpenCV and Python 3 : six end-to-end projects build using machine learning with OpenCV, Python, and TensorFlow (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789954906 , 1789954908
    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: Gain a working knowledge of advanced machine learning and explore Python's powerful tools for extracting data from images and videos Key Features Implement image classification and object detection using machine learning and deep learning Perform image classification, object detection, image segmentation, and other Computer Vision tasks Crisp content with a practical approach to solving real-world problems in Computer Vision Book Description Python is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries. This book will help you design and develop production-grade Computer Vision projects tackling real-world problems. With the help of this book, you will learn how to set up Anaconda and Python for the major OSes with cutting-edge third-party libraries for Computer Vision. You'll learn state-of-the-art techniques for classifying images, finding and identifying human postures, and detecting faces within videos. You will use powerful machine learning tools such as OpenCV, Dlib, and TensorFlow to build exciting projects such as classifying handwritten digits, detecting facial features,and much more. The book also covers some advanced projects, such as reading text from license plates from real-world images using Google's Tesseract software, and tracking human body poses using DeeperCut within TensorFlow. By the end of this book, you will have the expertise required to build your own Computer Vision projects using Python and its associated libraries. What you will learn Install and run major Computer Vision packages within Python Apply powerful support vector machines for simple digit classification Understand deep learning with TensorFlow Build a deep learning classifier for general images Use LSTMs for automated image captioning Read text from real-world images Extract human pose data from images Who this book is for Python programmers and machine learning developers who wish to build exciting Computer Vision projects using the power of machine learning and OpenCV will find this book useful. The only prerequisite for this book is that you should have a sound knowledge of Python programming.
    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/-/9781789954555/?ar
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