Multithreaded JavaScript
Traditionally, JavaScript has been a single-threaded language. Nearly all online forum posts, books, online documentation, and libraries refer to the language as single threaded. Thanks to recent advancements in the language-such as the Atomics and SharedArrayBuffers objects and Web Workers in the browser-JavaScript is now a multi-threaded language. These features will go down as being the biggest paradigm shift for the world's most popular programming language. Multithreaded JavaScript explores the various features that JavaScript runtimes have at their disposal for implementing multithreaded programming, providing both practical real-world examples, as well as reference material. Learn what multithreaded programming is and how you can benefit from it; Understand the differences between a web worker, a service worker, and a worker thread; Know when and when not to use threads in an application; Orchestrate communication between threads by leveraging the Atomics object; Build hig ...
Traditionally, JavaScript has been a single-threaded language. Nearly all online forum posts, books, online documentation, and libraries refer to the language as single threaded. Thanks to recent advancements in the language-such as the Atomics and SharedArrayBuffers objects and Web Workers in the browser-JavaScript is now a multi-threaded language. These features will go down as being the biggest paradigm shift for the world's most popular programming language. Multithreaded JavaScript explores the various features that JavaScript runtimes have at their disposal for implementing multithreaded programming, providing both practical real-world examples, as well as reference material. Learn what multithreaded programming is and how you can benefit from it; Understand the differences between a web worker, a service worker, and a worker thread; Know when and when not to use threads in an application; Orchestrate communication between threads by leveraging the Atomics object; Build hig ...
Learning Test-Driven Development
Your code is a testament to your skills as a developer. No matter what language you use, code should be clean, elegant, and uncluttered. By using test-driven development (TDD), you'll write code that's easy to understand, retains its elegance, and works for months, even years, to come. With this indispensable guide, you'll learn how to use TDD with three different languages: Go, JavaScript, and Python. Author Saleem Siddiqui shows you how to tackle domain complexity using a unit test-driven approach. TDD partitions requirements into small, implementable features, enabling you to solve problems irrespective of the languages and frameworks you use. With Learning Test-Driven Development at your side, you'll learn how to incorporate TDD into your regular coding practice. This book helps you: Use TDD's divide-and-conquer approach to tame domain complexity; Understand how TDD works across languages, testing frameworks, and domain concepts; Learn how TDD enables continuous integration; ...
Your code is a testament to your skills as a developer. No matter what language you use, code should be clean, elegant, and uncluttered. By using test-driven development (TDD), you'll write code that's easy to understand, retains its elegance, and works for months, even years, to come. With this indispensable guide, you'll learn how to use TDD with three different languages: Go, JavaScript, and Python. Author Saleem Siddiqui shows you how to tackle domain complexity using a unit test-driven approach. TDD partitions requirements into small, implementable features, enabling you to solve problems irrespective of the languages and frameworks you use. With Learning Test-Driven Development at your side, you'll learn how to incorporate TDD into your regular coding practice. This book helps you: Use TDD's divide-and-conquer approach to tame domain complexity; Understand how TDD works across languages, testing frameworks, and domain concepts; Learn how TDD enables continuous integration; ...
Docker Succinctly
Containers have revolutionized software development, allowing developers to bundle their applications with everything they need, from the operating system up, into a single package. Docker is one of the most popular platforms for containers, allowing them to be hosted on-premises or on the cloud, and to run on Linux, Windows, and Mac machines. With Docker Succinctly by Elton Stoneman, learn the basics of building Docker images, sharing them on the Docker Hub, orchestrating containers to deliver large applications, and much more. ...
Containers have revolutionized software development, allowing developers to bundle their applications with everything they need, from the operating system up, into a single package. Docker is one of the most popular platforms for containers, allowing them to be hosted on-premises or on the cloud, and to run on Linux, Windows, and Mac machines. With Docker Succinctly by Elton Stoneman, learn the basics of building Docker images, sharing them on the Docker Hub, orchestrating containers to deliver large applications, and much more. ...
Cloud Design Patterns
Cloud applications have a unique set of characteristics. They run on commodity hardware, provide services to untrusted users, and deal with unpredictable workloads. These factors impose a range of problems that you, as a designer or developer, need to resolve. Your applications must be resilient so that they can recover from failures, secure to protect services from malicious attacks, and elastic in order to respond to an ever changing workload. This guide demonstrates design patterns that can help you to solve the problems you might encounter in many different areas of cloud application development. Each pattern discusses design considerations, and explains how you can implement it using the features of Windows Azure. The patterns are grouped into categories: availability, data management, design and implementation, messaging, performance and scalability, resilience, management and monitoring, and security. You will also see more general guidance related to these areas of concern. It ...
Cloud applications have a unique set of characteristics. They run on commodity hardware, provide services to untrusted users, and deal with unpredictable workloads. These factors impose a range of problems that you, as a designer or developer, need to resolve. Your applications must be resilient so that they can recover from failures, secure to protect services from malicious attacks, and elastic in order to respond to an ever changing workload. This guide demonstrates design patterns that can help you to solve the problems you might encounter in many different areas of cloud application development. Each pattern discusses design considerations, and explains how you can implement it using the features of Windows Azure. The patterns are grouped into categories: availability, data management, design and implementation, messaging, performance and scalability, resilience, management and monitoring, and security. You will also see more general guidance related to these areas of concern. It ...
First Semester in Numerical Analysis with Python
The book is based on "First semester in Numerical Analysis with Julia". The contents of the original book are retained, while all the algorithms are implemented in Python (Version 3.8.0). Python is an open source (under OSI), interpreted, general-purpose programming language that has a large number of users around the world. Python is ranked the third in August 2020 by the TIOBE programming community index 2 , a measure of popularity of programming languages, and is the top-ranked interpreted language. We hope this book will better serve readers who are interested in a first course in Numerical Analysis, but are more familiar with Python for the implementation of the algorithms. The first chapter of the book has a self-contained tutorial for Python, including how to set up the computer environment. Anaconda, the open-source individual edition, is recommended for an easy installation of Python and effortless management of Python packages, and the Jupyter environment, a web-based inte ...
The book is based on "First semester in Numerical Analysis with Julia". The contents of the original book are retained, while all the algorithms are implemented in Python (Version 3.8.0). Python is an open source (under OSI), interpreted, general-purpose programming language that has a large number of users around the world. Python is ranked the third in August 2020 by the TIOBE programming community index 2 , a measure of popularity of programming languages, and is the top-ranked interpreted language. We hope this book will better serve readers who are interested in a first course in Numerical Analysis, but are more familiar with Python for the implementation of the algorithms. The first chapter of the book has a self-contained tutorial for Python, including how to set up the computer environment. Anaconda, the open-source individual edition, is recommended for an easy installation of Python and effortless management of Python packages, and the Jupyter environment, a web-based inte ...
Graph-Powered Machine Learning
Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural ...
Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You'll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro's extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural ...
First Semester in Numerical Analysis with Python
The book is based on "First semester in Numerical Analysis with Julia". The contents of the original book are retained, while all the algorithms are implemented in Python (Version 3.8.0). Python is an open source (under OSI), interpreted, general-purpose programming language that has a large number of users around the world. Python is ranked the third in August 2020 by the TIOBE programming community index 2 , a measure of popularity of programming languages, and is the top-ranked interpreted language. We hope this book will better serve readers who are interested in a first course in Numerical Analysis, but are more familiar with Python for the implementation of the algorithms. The first chapter of the book has a self-contained tutorial for Python, including how to set up the computer environment. Anaconda, the open-source individual edition, is recommended for an easy installation of Python and effortless management of Python packages, and the Jupyter environment, a web-based inte ...
The book is based on "First semester in Numerical Analysis with Julia". The contents of the original book are retained, while all the algorithms are implemented in Python (Version 3.8.0). Python is an open source (under OSI), interpreted, general-purpose programming language that has a large number of users around the world. Python is ranked the third in August 2020 by the TIOBE programming community index 2 , a measure of popularity of programming languages, and is the top-ranked interpreted language. We hope this book will better serve readers who are interested in a first course in Numerical Analysis, but are more familiar with Python for the implementation of the algorithms. The first chapter of the book has a self-contained tutorial for Python, including how to set up the computer environment. Anaconda, the open-source individual edition, is recommended for an easy installation of Python and effortless management of Python packages, and the Jupyter environment, a web-based inte ...
Blazor, A Beginners Guide
Blazor, A Beginners Guide is intended for developers with some .NET experience. If you're coming from a non-.NET development background, you may learn some .NET basics along the way, but supplemental material around C# and .NET would be of great help to you. The book begins with the author's own perspective on WebAssembly, why it's important to have choices in web development and offers some insight on why Blazor was created. You'll then step through an overview where you'll get familiar with the framework and technology stack. As the book progresses you will see working examples that teach core concepts broadly. Later in the book, topics that require a deeper knowledge are revisited to grasp the fine details so you can make informed decisions about how your apps and components are built. The intent of the book's layout is to get you productive quickly. You should be able to navigate and create Blazor projects with ease as you read through the chapters versus having to complete t ...
Blazor, A Beginners Guide is intended for developers with some .NET experience. If you're coming from a non-.NET development background, you may learn some .NET basics along the way, but supplemental material around C# and .NET would be of great help to you. The book begins with the author's own perspective on WebAssembly, why it's important to have choices in web development and offers some insight on why Blazor was created. You'll then step through an overview where you'll get familiar with the framework and technology stack. As the book progresses you will see working examples that teach core concepts broadly. Later in the book, topics that require a deeper knowledge are revisited to grasp the fine details so you can make informed decisions about how your apps and components are built. The intent of the book's layout is to get you productive quickly. You should be able to navigate and create Blazor projects with ease as you read through the chapters versus having to complete t ...
Math for Deep Learning
Deep learning is everywhere, making this powerful driver of AI something more STEM professionals need to know. Learning which library commands to use is one thing, but to truly understand the discipline, you need to grasp the mathematical concepts that make it tick. This book will give you a working knowledge of topics in probability, statistics, linear algebra, and differential calculus - the essential math needed to make deep learning comprehensible, which is key to practicing it successfully. Each of the four subfields are contextualized with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. Chapters build upon one another, with foundational topics such as Bayes' theorem followed by more advanced concepts, like training neural networks using vectors, matrices, and derivatives of functions. You'll ultimately put all this math to use as you explore and implement deep learning algorithms, including backp ...
Deep learning is everywhere, making this powerful driver of AI something more STEM professionals need to know. Learning which library commands to use is one thing, but to truly understand the discipline, you need to grasp the mathematical concepts that make it tick. This book will give you a working knowledge of topics in probability, statistics, linear algebra, and differential calculus - the essential math needed to make deep learning comprehensible, which is key to practicing it successfully. Each of the four subfields are contextualized with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. Chapters build upon one another, with foundational topics such as Bayes' theorem followed by more advanced concepts, like training neural networks using vectors, matrices, and derivatives of functions. You'll ultimately put all this math to use as you explore and implement deep learning algorithms, including backp ...
Practical SQL, 2nd Edition
Practical SQL is an approachable and fast-paced guide to SQL (Structured Query Language), the standard programming language for defining, organizing, and exploring data in relational databases. Anthony DeBarros, a journalist and data analyst, focuses on using SQL to find the story within your data. The examples and code use the open-source database PostgreSQL and its companion pgAdmin interface, and the concepts you learn will apply to most database management systems, including MySQL, Oracle, SQLite, and others. You'll first cover the fundamentals of databases and the SQL language, then build skills by analyzing data from real-world datasets such as US Census demographics, New York City taxi rides, and earthquakes from US Geological Survey. Each chapter includes exercises and examples that teach even those who have never programmed before all the tools necessary to build powerful databases and access information quickly and efficiently. You'll learn how to: Create databases and ...
Practical SQL is an approachable and fast-paced guide to SQL (Structured Query Language), the standard programming language for defining, organizing, and exploring data in relational databases. Anthony DeBarros, a journalist and data analyst, focuses on using SQL to find the story within your data. The examples and code use the open-source database PostgreSQL and its companion pgAdmin interface, and the concepts you learn will apply to most database management systems, including MySQL, Oracle, SQLite, and others. You'll first cover the fundamentals of databases and the SQL language, then build skills by analyzing data from real-world datasets such as US Census demographics, New York City taxi rides, and earthquakes from US Geological Survey. Each chapter includes exercises and examples that teach even those who have never programmed before all the tools necessary to build powerful databases and access information quickly and efficiently. You'll learn how to: Create databases and ...