Pro Grunt.js
Pro Grunt.js gets you quickly up-to-speed with this popular JavaScript-based task runner. Author James Cryer takes you from initial installation all the way through to authoring successful plugins. Using hands-on examples you will learn about CSS linting, combination, compilation and minification; JavaScript linting, AMD modules, and templates; image processing and optimizing; and creating a local server. You will then move on to more advanced topics such as LiveReload, Test Automation, Modular configuration, and combining tasks. Finally you will see how to scaffold plugins using Grunt init and Yeoman, how to test them with Mocha and Chai, and how to publish them so others can use them. Start using Grunt.js to improve your workflows by using Pro Grunt.js today. ...
Pro Grunt.js gets you quickly up-to-speed with this popular JavaScript-based task runner. Author James Cryer takes you from initial installation all the way through to authoring successful plugins. Using hands-on examples you will learn about CSS linting, combination, compilation and minification; JavaScript linting, AMD modules, and templates; image processing and optimizing; and creating a local server. You will then move on to more advanced topics such as LiveReload, Test Automation, Modular configuration, and combining tasks. Finally you will see how to scaffold plugins using Grunt init and Yeoman, how to test them with Mocha and Chai, and how to publish them so others can use them. Start using Grunt.js to improve your workflows by using Pro Grunt.js today. ...
Pro REST API Development with Node.js
Pro REST API Development with Node.js is your guide to managing and understanding the full capabilities of successful REST development. API design is a hot topic in the programming world, but not many resources exist for developers to really understand how you can leverage the advantages. This book will provide a brief background on REST and the tools it provides (well known and not so well known). Understand how there is more to REST than just JSON and URLs. You will then cover and compare the maintained modules currently available in the npm community, including Express, Restify, Vatican, and Swagger. Finally you will code an example API from start to finish, using a subset of the tools covered. The Node community is currently flooded with modules; some of them are published once and never updated again - cluttering the entire universe of packages. Pro REST API Development with Node.js shines light into that black hole of modules for the developers trying to create an API. Unde ...
Pro REST API Development with Node.js is your guide to managing and understanding the full capabilities of successful REST development. API design is a hot topic in the programming world, but not many resources exist for developers to really understand how you can leverage the advantages. This book will provide a brief background on REST and the tools it provides (well known and not so well known). Understand how there is more to REST than just JSON and URLs. You will then cover and compare the maintained modules currently available in the npm community, including Express, Restify, Vatican, and Swagger. Finally you will code an example API from start to finish, using a subset of the tools covered. The Node community is currently flooded with modules; some of them are published once and never updated again - cluttering the entire universe of packages. Pro REST API Development with Node.js shines light into that black hole of modules for the developers trying to create an API. Unde ...
Node.js in Action, 2nd Edition
You already know JavaScript. The trick to mastering Node.js is learning how to build applications that fully exploit its powerful asynchronous event handling and non-blocking I/O features. The Node server radically simplifies event-driven real-time apps like chat, games, and live data analytics, and with its incredibly rich ecosystem of modules, tools, and libraries, it's hard to beat! Based on the bestselling first edition, Node.js in Action, 2nd Edition is a completely new book. Packed with practical examples, it teaches you how to create high-performance web servers using JavaScript and Node. You'll master key design concepts such as asynchronous programming, state management, and event-driven programming. And you'll learn to put together MVC servers using Express and Connect, design web APIs, and set up the perfect production environment to build, lint, and test. ...
You already know JavaScript. The trick to mastering Node.js is learning how to build applications that fully exploit its powerful asynchronous event handling and non-blocking I/O features. The Node server radically simplifies event-driven real-time apps like chat, games, and live data analytics, and with its incredibly rich ecosystem of modules, tools, and libraries, it's hard to beat! Based on the bestselling first edition, Node.js in Action, 2nd Edition is a completely new book. Packed with practical examples, it teaches you how to create high-performance web servers using JavaScript and Node. You'll master key design concepts such as asynchronous programming, state management, and event-driven programming. And you'll learn to put together MVC servers using Express and Connect, design web APIs, and set up the perfect production environment to build, lint, and test. ...
Machine Learning with TensorFlow
TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine. Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. ...
TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine. Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. ...
Machine Learning with Python Cookbook
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. Vectors, matrices, and arrays; Handling numerical and categorical data, text, images, and dates and times; Dimensionality reduction using feature extracti ...
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. Vectors, matrices, and arrays; Handling numerical and categorical data, text, images, and dates and times; Dimensionality reduction using feature extracti ...
Optimizing Data-to-Learning-to-Action
Apply a powerful new approach and method that ensures continuous performance improvement for your business. You will learn how to determine and value the people, process, and technology-based solutions that will optimize your organization's data-to-learning-to-action processes. This book describes in detail how to holistically optimize the chain of activities that span from data to learning to decisions to actions, an imperative for achieving outstanding performance in today's business environment. Adapting and integrating insights from decision science, constraint theory, and process improvement, the book provides a method that is clear, effective, and can be applied to nearly every business function and sector. You will learn how to systematically work backwards from decisions to data, estimate the flow of value along the chain, and identify the inevitable value bottlenecks. And, importantly, you will learn techniques for quantifying the value that can be attained by successful ...
Apply a powerful new approach and method that ensures continuous performance improvement for your business. You will learn how to determine and value the people, process, and technology-based solutions that will optimize your organization's data-to-learning-to-action processes. This book describes in detail how to holistically optimize the chain of activities that span from data to learning to decisions to actions, an imperative for achieving outstanding performance in today's business environment. Adapting and integrating insights from decision science, constraint theory, and process improvement, the book provides a method that is clear, effective, and can be applied to nearly every business function and sector. You will learn how to systematically work backwards from decisions to data, estimate the flow of value along the chain, and identify the inevitable value bottlenecks. And, importantly, you will learn techniques for quantifying the value that can be attained by successful ...
Mastering Machine Learning with scikit-learn
This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices. ...
This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices. ...
Learning TypeScript 2.x, 2nd Edition
TypeScript is an open source and cross-platform statically typed superset of JavaScript that compiles to plain JavaScript and runs in any browser or host. This book is a step-by-step guide that will take you through the use and benefits of TypeScript with the help of practical examples. You will start off by understanding the basics as well as the new features of TypeScript 2.x. Then, you will learn how to work with functions and asynchronous programming APIs. You will continue by learning how to resolve runtime issues and how to implement TypeScript applications using the Object-oriented programming (OOP) and functional programming (FP) paradigms. Later, you will automate your development workflow with the help of tools such as Webpack. Towards the end of this book, you will delve into some real-world scenarios by implementing some full-stack TypeScript applications with Node.js, React and Angular as well as how to optimize and test them. Finally, you will be introduced to the inte ...
TypeScript is an open source and cross-platform statically typed superset of JavaScript that compiles to plain JavaScript and runs in any browser or host. This book is a step-by-step guide that will take you through the use and benefits of TypeScript with the help of practical examples. You will start off by understanding the basics as well as the new features of TypeScript 2.x. Then, you will learn how to work with functions and asynchronous programming APIs. You will continue by learning how to resolve runtime issues and how to implement TypeScript applications using the Object-oriented programming (OOP) and functional programming (FP) paradigms. Later, you will automate your development workflow with the help of tools such as Webpack. Towards the end of this book, you will delve into some real-world scenarios by implementing some full-stack TypeScript applications with Node.js, React and Angular as well as how to optimize and test them. Finally, you will be introduced to the inte ...
Hands-On Automated Machine Learning
AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners' work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you'll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you'll be able to incorporate automation tasks using practical datasets. You can leverage your lea ...
AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners' work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you'll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you'll be able to incorporate automation tasks using practical datasets. You can leverage your lea ...
Machine Learning Solutions
Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You'll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you'll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, ...
Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You'll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you'll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, ...