Github Actions Cookbook
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GitHub Actions Cookbook
Author: Michael Kaufmann
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-04-30
Authored by a Microsoft Regional Director, this book shows you how to leverage the power of the community-driven GitHub Actions workflow platform to automate repetitive engineering tasks Key Features Automate CI/CD workflows and deploy securely to cloud providers like Azure, AWS, or GCP using OpenID Create your own custom actions with Docker, JavaScript programming, or shell scripts and share them with others Discover ways to automate complex scenarios beyond the basic ones documented in GitHub Book DescriptionSay goodbye to tedious tasks! GitHub Actions is a powerful workflow engine that automates everything in the GitHub ecosystem, letting you focus on what matters most. This book explains the GitHub Actions workflow syntax, the different kinds of actions, and how GitHub-hosted and self-hosted workflow runners work. You’ll get tips on how to author and debug GitHub Actions and workflows with Visual Studio Code (VS Code), run them locally, and leverage the power of GitHub Copilot. The book uses hands-on examples to walk you through real-world use cases that will help you automate the entire release process. You’ll cover everything, from automating the generation of release notes to building and testing your software and deploying securely to Azure, Amazon Web Services (AWS), or Google Cloud using OpenID Connect (OIDC), secrets, variables, environments, and approval checks. The book goes beyond CI/CD by demonstrating recipes to execute IssueOps and automate other repetitive tasks using the GitHub CLI, GitHub APIs and SDKs, and GitHub Token. You’ll learn how to build your own actions and reusable workflows to share building blocks with the community or within your organization. By the end of this GitHub book, you'll have gained the skills you need to automate tasks and work with remarkable efficiency and agility.What you will learn Author and debug GitHub Actions workflows with VS Code and Copilot Run your workflows on GitHub-provided VMs (Linux, Windows, and macOS) or host your own runners in your infrastructure Understand how to secure your workflows with GitHub Actions Boost your productivity by automating workflows using GitHub's powerful tools, such as the CLI, APIs, SDKs, and access tokens Deploy to any cloud and platform in a secure and reliable way with staged or ring-based deployments Who this book is for This book is for anyone looking for a practical approach to learning GitHub Actions, regardless of their experience level. Whether you're a software developer, a DevOps engineer, anyone who has already experimented with Actions, or someone completely new to CI/CD tools like Jenkins or Azure Pipelines, you’ll find expert insights in this book. Basic knowledge of using Git and command lines is a must.
TensorFlow 2 Reinforcement Learning Cookbook
Author: Praveen Palanisamy
language: en
Publisher: Packt Publishing Ltd
Release Date: 2021-01-15
Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning Key FeaturesDevelop and deploy deep reinforcement learning-based solutions to production pipelines, products, and servicesExplore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic methodCustomize and build RL-based applications for performing real-world tasksBook Description With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch. What you will learnBuild deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras APIImplement state-of-the-art deep reinforcement learning algorithms using minimal codeBuild, train, and package deep RL agents for cryptocurrency and stock tradingDeploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud servicesSpeed up agent development using distributed DNN model trainingExplore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)Who this book is for The book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x.
Game Physics Cookbook
Collision Detection and Rigid body physics for Game Development Key Features Get a comprehensive coverage of techniques to create high performance collision detection in games Learn the core mathematics concepts and physics involved in depicting collision detection for your games Get a hands-on experience of building a rigid body physics engine Book DescriptionPhysics is really important for game programmers who want to add realism and functionality to their games. Collision detection in particular is a problem that affects all game developers, regardless of the platform, engine, or toolkit they use. This book will teach you the concepts and formulas behind collision detection. You will also be taught how to build a simple physics engine, where Rigid Body physics is the main focus, and learn about intersection algorithms for primitive shapes. You’ll begin by building a strong foundation in mathematics that will be used throughout the book. We’ll guide you through implementing 2D and 3D primitives and show you how to perform effective collision tests for them. We then pivot to one of the harder areas of game development—collision detection and resolution. Further on, you will learn what a Physics engine is, how to set up a game window, and how to implement rendering. We’ll explore advanced physics topics such as constraint solving. You’ll also find out how to implement a rudimentary physics engine, which you can use to build an Angry Birds type of game or a more advanced game. By the end of the book, you will have implemented all primitive and some advanced collision tests, and you will be able to read on geometry and linear Algebra formulas to take forward to your own games!What you will learn Implement fundamental maths so you can develop solid game physics Use matrices to encode linear transformations Know how to check geometric primitives for collisions Build a Physics engine that can create realistic rigid body behavior Understand advanced techniques, including the Separating Axis Theorem Create physically accurate collision reactions Explore spatial partitioning as an acceleration structure for collisions Resolve rigid body collisions between primitive shapes Who this book is for This book is for beginner to intermediate game developers. You don’t need to have a formal education in games—you can be a hobbyist or indie developer who started making games with Unity 3D.