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  • Ciaburro, Giuseppe  (6)
  • Anggoro, Wisnu  (4)
  • Birmingham, UK : Packt Publishing  (10)
  • Electronic books ; local  (10)
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  • Monografische Reihe
  • 1
    Online Resource
    Online Resource
    Python machine learning cookbook : over 100 recipes to progress from smart data analytics to deep learning using real-world datasets (2019)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789800753 , 1789800757
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Edition: Second edition.
    Keywords: Python (Computer program language) ; Machine learning ; Electronic books ; Electronic books ; local
    Abstract: Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Key Features Learn and implement machine learning algorithms in a variety of real-life scenarios Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques Find easy-to-follow code solutions for tackling common and not-so-common challenges Book Description This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. What you will learn Use predictive modeling and apply it to real-world problems Explore data visualization techniques to interact with your data Learn how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Get well versed with reinforcement learning, automated ML, and transfer learning Work with image data and build systems for image recognition and biometric face recognition Use deep neural networks to build an optical character recognition system Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is w...
    Note: Previous edition published: 2016. - Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed May 15, 2019)
    URL: https://learning.oreilly.com/library/view/-/9781789808452/?ar
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  • 2
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    Hands-on data warehousing with Azure Data Factory : ETL techniques to load and transform data from various sources, both on-premises and on cloud (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781789130096 , 1789130093
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Windows Azure ; Data warehousing ; Database management ; Information storage and retrieval systems ; Data processing ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Leverage the power of Microsoft Azure Data Factory v2 to build hybrid data solutions About This Book Combine the power of Azure Data Factory v2 and SQL Server Integration Services Design and enhance performance and scalability of a modern ETL hybrid solution Interact with the loaded data in data warehouse and data lake using Power BI Who This Book Is For This book is for you if you are a software professional who develops and implements ETL solutions using Microsoft SQL Server or Azure cloud. It will be an added advantage if you are a software engineer, DW/ETL architect, or ETL developer, and know how to create a new ETL implementation or enhance an existing one with ADF or SSIS. What You Will Learn Understand the key components of an ETL solution using Azure Data Factory and Integration Services Design the architecture of a modern ETL hybrid solution Implement ETL solutions for both on-premises and Azure data Improve the performance and scalability of your ETL solution Gain thorough knowledge of new capabilities and features added to Azure Data Factory and Integration Services In Detail ETL is one of the essential techniques in data processing. Given data is everywhere, ETL will always be the vital process to handle data from different sources. Hands-On Data Warehousing with Azure Data Factory starts with the basic concepts of data warehousing and ETL process. You will learn how Azure Data Factory and SSIS can be used to understand the key components of an ETL solution. You will go through different services offered by Azure that can be used by ADF and SSIS, such as Azure Data Lake Analytics, Machine Learning and Databrick's Spark with the help of practical examples. You will explore how to design and implement ETL hybrid solutions using different integration services with a step-by-step approach. Once you get to grips with all this, you will use Power BI to interact with data coming from different sources in order to reveal valuable insights. By the end of this book, you will not only learn how to build your own ETL solutions but also address the key challenges that are faced while building them. Style and approach A step-by-step guide to develop data movement code using SSIS, Azure Data Factory, and database stored procedures for implementing intelligent BI solutions. 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 ...
    Note: Description based on online resource; title from title page (Safari, viewed June 29, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781789137620/?ar
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  • 3
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    Regression analysis with R : design and develop statistical nodes to identify unique relationships within data at scale (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788622707 , 1788622707
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Regression analysis ; R (Computer program language) ; Statistical hypothesis testing ; Econometric models ; Electronic books ; Electronic books ; local
    Abstract: Build effective regression models in R to extract valuable insights from real data About This Book Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values From Simple Linear Regression to Logistic Regression - this book covers all regression techniques and their implementation in R A complete guide to building effective regression models in R and interpreting results from them to make valuable predictions Who This Book Is For This book is intended for budding data scientists and data analysts who want to implement regression analysis techniques using R. If you are interested in statistics, data science, machine learning and wants to get an easy introduction to the topic, then this book is what you need! Basic understanding of statistics and math will help you to get the most out of the book. Some programming experience with R will also be helpful What You Will Learn Get started with the journey of data science using Simple linear regression Deal with interaction, collinearity and other problems using multiple linear regression Understand diagnostics and what to do if the assumptions fail with proper analysis Load your dataset, treat missing values, and plot relationships with exploratory data analysis Develop a perfect model keeping overfitting, under-fitting, and cross-validation into consideration Deal with classification problems by applying Logistic regression Explore other regression techniques - Decision trees, Bagging, and Boosting techniques Learn by getting it all in action with the help of a real world case study. In Detail Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are - supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process - l...
    Note: Description based on online resource; title from title page (Safari, viewed February 27, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788627306/?ar
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  • 4
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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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  • 5
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    C++ data structures and algorithms : learn how to write efficient code to build scalable and robust applications in C++ (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788831970 , 1788831977
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: C++ (Computer program language) ; Data structures (Computer science) ; Computer algorithms ; Electronic books ; Electronic books ; local
    Abstract: Learn how to build efficient, secure and robust code in C++ by using data structures and algorithms - the building blocks of C++ About This Book Use data structures such as arrays, stacks, trees, lists, and graphs with real-world examples Learn the functional and reactive implementations of the traditional data structures Explore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner Who This Book Is For This book is for developers who would like to learn the Data Structures and Algorithms in C++. Basic C++ programming knowledge is expected. What You Will Learn Know how to use arrays and lists to get better results in complex scenarios Build enhanced applications by using hashtables, dictionaries, and sets Implement searching algorithms such as linear search, binary search, jump search, exponential search, and more Have a positive impact on the efficiency of applications with tree traversal Explore the design used in sorting algorithms like Heap sort, Quick sort, Merge sort and Radix sort Implement various common algorithms in string data types Find out how to design an algorithm for a specific task using the common algorithm paradigms In Detail C++ is a general-purpose programming language which has evolved over the years and is used to develop software for many different sectors. This book will be your companion as it takes you through implementing classic data structures and algorithms to help you get up and running as a confident C++ programmer. We begin with an introduction to C++ data structures and algorithms while also covering essential language constructs. Next, we will see how to store data using linked lists, arrays, stacks, and queues. Then, we will learn how to implement different sorting algorithms, such as quick sort and heap sort. Along with these, we will dive into searching algorithms such as linear search, binary search and more. Our next mission will be to attain high performance by implementing algorithms to string datatypes and implementing hash structures in algorithm design. We'll also analyze Brute Force algorithms, Greedy algorithms, and more. By the end of the book, you'll know how to build components that are easy to understand, debug, and use in different applications. Style and approach Readers will be taken through an indispensable list of data structures and algorithms so they can confidently begin coding in C++. Downloading the example code for this book Y...
    Note: Description based on online resource; title from title page (Safari, viewed May 23, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788835213/?ar
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  • 6
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    Functional C# : uncover the secrets of functional programming using C# and change the way you approach your applications forever (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785881039 , 1785881035
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    DDC: 005.114
    Keywords: C# (Computer program language) ; Functional programming (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Uncover the secrets of functional programming using C# and change the way you approach your applications forever About This Book This book focuses on the functional paradigm of C#, which will give you a whole new angle on coding with C# It illustrates the advantages that functional programming brings to the table and the associated coding benefits This practical guide covers all the aspects of functional programming and provides solutions that can be applied in business scenarios Who This Book Is For This book is suitable for C# developers with basic prior knowledge of C# and with no functional programming experience at all. What You Will Learn Develop an application using the functional approach Implement unit testing to functionally program code Create efficient code using functional programming Work through a LINQ query so you can work with data Compose asynchronous programs to create a responsive application Use recursion in function programming in order to simplify code Optimize the program code using Laziness and Caching Techniques In Detail Functional programming makes your application faster, improves performance, and increases your productivity. C# code is written at a higher level of abstraction, so that code will be closer to business requirements, abstracting away many low-level implementation details. This book bridges the language gap for C# developers by showing you how to create and consume functional constructs in C#. We also bridge the domain gap by showing how functional constructs can be applied in business scenarios. We'll take you through lambda expressions and extension methods, and help you develop a deep understanding of the concepts and practices of LINQ and recursion in C#. By the end of the book, you will be able to write code using the best approach and will be able to perform unit testing in functional programming, changing how you write your applications and revolutionizing your projects. Style and approach This book takes a pragmatic approach and shows you techniques to write better functional constructs in C#. We'll also show you how these concepts can be applied in business scenarios.
    Note: Description based on online resource; title from cover (Safari, viewed January 25, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781785282225/?ar
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  • 7
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    MATLAB for machine learning : functions, algorithms, and use cases (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788399395 , 1788399390
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: MATLAB ; Machine learning ; Neural networks (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into rea...
    Note: Description based on online resource; title from title page (Safari, viewed September 25, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781788398435/?ar
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  • 8
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    Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788399418 , 1788399412
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: R (Computer program language) ; Neural networks (Computer science) ; Machine learning ; Artificial intelligence ; Electronic books ; Electronic books ; local
    Abstract: Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.
    Note: Description based on online resource; title from title page (Safari, viewed October 31, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781788397872/?ar
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  • 9
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    Learning.C++ functional programming : metaprogramming, concurrency, lazy evaluation, and more (2017)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781787280588 , 1787280586
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: C++ (Computer program language) ; Functional programming (Computer science) ; Electronic books ; Electronic books ; local
    Abstract: Apply Functional Programming techniques to C++ to build highly modular, testable, and reusable code About This Book Modularize your applications and make them highly reusable and testable Get familiar with complex concepts such as metaprogramming, concurrency, and immutability A highly practical guide to building functional code in C++ filled with lots of examples and real-world use cases Who This Book Is For This book is for C++ developers comfortable with OOP who are interested in learning how to apply the functional paradigm to create robust and testable apps. What You Will Learn Get to know the difference between imperative and functional approaches See the use of first-class functions and pure functions in a functional style Discover various techniques to apply immutable state to avoid side effects Design a recursive algorithm effectively Create faster programs using lazy evaluation Structure code using design patterns to make the design process easier Use concurrency techniques to develop responsive software Learn how to use the C++ Standard Template Library and metaprogramming in a functional way to improve code optimization In Detail Functional programming allows developers to divide programs into smaller, reusable components that ease the creation, testing, and maintenance of software as a whole. Combined with the power of C++, you can develop robust and scalable applications that fulfill modern day software requirements. This book will help you discover all the C++ 17 features that can be applied to build software in a functional way. The book is divided into three modules - the first introduces the fundamentals of functional programming and how it is supported by modern C++. The second module explains how to efficiently implement C++ features such as pure functions and immutable states to build robust applications. The last module describes how to achieve concurrency and apply design patterns to enhance your application's performance. Here, you will also learn to optimize code using metaprogramming in a functional way. By the end of the book, you will be familiar with the functional approach of programming and will be able to use these techniques on a daily basis. Style and approach This book uses a module-based approach, where each module will cover important aspects of functional programming in C++ and will help you develop efficient and robust applications through gaining a practical understanding.
    Note: Description based on online resource; title from title page (Safari, viewed August 31, 2017)
    URL: https://learning.oreilly.com/library/view/-/9781787281974/?ar
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  • 10
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    Boost.Asio C++ network programming : learn effective C++ network programming with Boost.Asio and become a proficient C++ network programmer (2015)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781785289095 , 1785289098
    Language: English
    Pages: 1 online resource (1 volume) , illustrations.
    Edition: Second edition.
    Series Statement: Community experience distilled
    DDC: 005.13/3
    Keywords: C++ (Computer program language) ; Object-oriented programming (Computer science) ; Computer networks ; Electronic books ; Electronic books ; local
    Abstract: Learn effective C++ network programming with Boost.Asio and become a proficient C++ network programmer About This Book Learn efficient C++ network programming with minimum coding using Boost.Asio Your one-stop destination to everything related to the Boost.Asio library Explore the fundamentals of networking to choose designs with more examples, and learn the basics of Boost.Asio Who This Book Is For This book is for C++ Network programmers with basic knowledge of network programming, but no knowledge of how to use Boost.Asio for network programming. What You Will Learn Prepare the tools to simplify network programming in C++ using Boost.Asio Explore the networking concepts of IP addressing, TCP/IP ports and protocols, and LAN topologies Get acquainted with the usage of the Boost libraries Get to know more about the content of Boost.Asio network programming and Asynchronous programming Establish communication between client and server by creating client-server application Understand the various functions inside Boost.Asio C++ libraries to delve into network programming Discover how to debug and run the code successfully In Detail Boost.Asio is a C++ library used for network programming operations. Organizations use Boost because of its productivity. Use of these high-quality libraries speed up initial development, result in fewer bugs, reduce reinvention-of-the-wheel, and cut long-term maintenance costs. Using Boost libraries gives an organization a head start in adopting new technologies. This book will teach you C++ Network programming using synchronous and asynchronous operations in Boost.Asio with minimum code, along with the fundamentals of Boost, server-client applications, debugging, and more. You will begin by preparing and setting up the required tools to simplify your network programming in C++ with Boost.Asio. Then you will learn about the basic concepts in networking such as IP addressing, TCP/IP protocols, and LAN with its topologies. This will be followed by an overview of the Boost libraries and their usage. Next you will get to know more about Boost.Asio and its concepts related to network programming. We will then go on to create a client-server application, helping you to understand the networking concepts. Moving on, you will discover how to use all the functions inside the Boost.Asio C++ libraries. Lastly, you will understand how to debug the code if there are errors found and will run the code successfully. Style and approa...
    Note: Includes index. - Description based on online resource; title from cover page (Safari, viewed October 7, 2015)
    URL: https://learning.oreilly.com/library/view/-/9781785283079/?ar
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