Books

"How Does Computer Science Work?" answers 28 key questions every software engineer should know, unraveling the technologies that shape our digital world. From how browsers work and cryptography secures data to AI, Web3, and quantum computing, this book bridges curiosity and understanding. Perfect for students, self-taught programmers, and seasoned developers, it’s your ultimate guide to mastering foundational concepts and cutting-edge innovations in computer science.

Learn how to use Microsoft Dynamics 365 Business Central development tools by exploring their strengths to address functional business requirements and extend Business Central with custom functionality using the AL language in Visual Studio Code.

This guide empowers you to conquer the challenges of building scalable web apps. You'll gain a deep understanding of key concepts, explore implementation techniques, and learn how to effectively supervise your applications for optimal performance.

This guide helps you master DynamoDB, the serverless NoSQL database built for high performance at any scale. Authored by AWS experts, it covers core features, data modeling, and advanced topics, enabling you to build state-of-the-art applications.

This book will help you implement sustainable software practices by exploring key principles and real-world examples. You'll gain the knowledge you need to navigate the intersection of software engineering and environmental sustainability.

Elevate your Microsoft Power Apps skills with this hands-on cookbook, packed with recipes for building efficient, scalable apps, automating workflows, including RPA and leveraging the latest features like AI-powered Copilot and Power Pages.

Dive into Microsoft Teams mastery with this comprehensive exam guide. From administration to security, collaboration, and troubleshooting, gain practical skills for exam success and real-world application.x

This book explores the potential of ChatGPT-powered chatbots and conversational AI. You'll delve into understanding the technology, its implementation, and practical use cases, while leveraging AI-powered conversations.

With this book, you'll learn the fundamentals of TDD and discover end-to-end testing using Protractor, Cypress, and Playwright. Moreover, you'll improve your development process with Angular TDD best practices.

Hone your machine learning skills to unlock robust models with less data through active machine learning. Tame messy datasets, conquer concept drift, and drive ML productivity with Python's active learning toolkit.

Read the Bible. In a year. With a friendly companion to help you get the big picture.

This third edition is updated with TensorFlow 2 and the latest additions to scikit-learn. Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: reinforcement learning and generative adversarial networks.

With six new chapters, Deep Reinforcement Learning Hands-On Second edition is completely updated and expanded with the very latest reinforcement learning (RL) tools and techniques, providing you with an introduction to RL, as well as the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

In this book, we will show you how to implement a series of best practices and design patterns to help you create efficient and robust Node.js applications with ease. We start by teaching you how to master the basics of Node.js development, such as its module systems and its event-driven asynchronous programming style. Next, you will learn how creational, structural, and behavioral design patterns can help you create modular and extensible software. Finally, we’ll show you how Node.js applications can be distributed and scaled to create complex software architectures.

This thoroughly revised and expanded second edition demonstrates on over 800 pages how machine learning can add value to algorithmic trading in a practical yet comprehensive way. It has four parts that cover how to work with a diverse set of market, fundamental, and alternative data sources, design ML solutions for real-world trading challenges, and manage the strategy development process from idea to backtesting and evaluation.