Implement continuous integration/continuous delivery (CI/CD) workflows for any application you develop through GitHub Actions. This book will give you an in-depth idea of implementation patterns, solutions for different technology builds, guidelines to implement your own custom components as actions, and usage of features available with GitHub Actions workflows, to set up CI/CD for your repositories. Hands-on GitHub Actions starts with an introduction to GitHub actions that gives an overview on CI/CD followed by an introduction to its workflows. Next, you will learn how to use variables in a GitHub workflow along with tokens via a REST API. Further, you will explore artifacts and caching dependencies in GitHub and use artifacts in subsequent jobs. Using self-hosted runners is discussed next where you will set up your own hardware and software to run GitHub actions. You will go through publishing packages and migrate to Azure DevOps Pipelines. Along the way, you will use Redis service and PostgreSQL service containers and create custom actions. Finally, you will work with GitHub apps and understand the syntax reference for GitHub Actions and workflows. You will: Create workflows for any platform and any language with GitHub Actions Develop custom GitHub actions to enhance features and usage of database and service containers Use hosted runners and create self-hosted runners for GitHub workflows Use GitHub Package registry with GitHub Actions to share and use packages.

Implement continuous integration/continuous delivery (CI/CD) workflows for any application you develop through GitHub Actions. This book will give you an in-depth idea of implementation patterns, solutions for different technology builds, guidelines to implement your own custom components as actions, and usage of features available with GitHub Actions workflows, to set up CI/CD for your repositories. Hands-on GitHub Actions starts with an introduction to GitHub actions that gives an overview on CI/CD followed by an introduction to its workflows. Next, you will learn how to use variables in a GitHub workflow along with tokens via a REST API. Further, you will explore artifacts and caching dependencies in GitHub and use artifacts in subsequent jobs. Using self-hosted runners is discussed next where you will set up your own hardware and software to run GitHub actions. You will go through publishing packages and migrate to Azure DevOps Pipelines. Along the way, you will use Redis service and PostgreSQL service containers and create custom actions. Finally, you will work with GitHub apps and understand the syntax reference for GitHub Actions and workflows. What You Will Learn Create workflows for any platform and any language with GitHub Actions Develop custom GitHub actions to enhance features and usage of database and service containers Use hosted runners and create self-hosted runners for GitHub workflows Use GitHub Package registry with GitHub Actions to share and use packages Who This Book Is For DevOps teams who want to build quality CI/CD workflows.

Build, test, and deploy code right from your GitHub repository by automating, customizing, and executing software development workflows with GitHub Actions Key FeaturesEnhance your CI/CD and DevOps workflows using GitHub ActionsDiscover how to create custom GitHub Actions using Docker and JavaScriptGet up and running with building a CI/CD pipeline effectivelyBook Description GitHub Actions is one of the most popular products that enables you to automate development tasks and improve your software development workflow. Automating Workflows with GitHub Actions uses real-world examples to help you automate everyday tasks and use your resources efficiently. This book takes a practical approach to helping you develop the skills needed to create complex YAML files to automate your daily tasks. You'll learn how to find and use existing workflows, allowing you to get started with GitHub Actions right away. Moving on, you'll discover complex concepts and practices such as self-hosted runners and writing workflow files that leverage other platforms such as Docker as well as programming languages such as Java and JavaScript. As you advance, you'll be able to write your own JavaScript, Docker, and composite run steps actions, and publish them in GitHub Marketplace! You'll also find instructions to migrate your existing CI/CD workflows into GitHub Actions from platforms like Travis CI and GitLab. Finally, you'll explore tools that'll help you stay informed of additions to GitHub Actions along with finding technical support and staying engaged with the community. By the end of this GitHub book, you'll have developed the skills and experience needed to build and maintain your own CI/CD pipeline using GitHub Actions. What you will learnGet to grips with the basics of GitHub and the YAML syntaxUnderstand key concepts of GitHub ActionsFind out how to write actions for JavaScript and Docker environmentsDiscover how to create a self-hosted runnerMigrate from other continuous integration and continuous delivery (CI/CD) platforms to GitHub ActionsCollaborate with the GitHub Actions community and find technical help to navigate technical difficultiesPublish your workflows in GitHub MarketplaceWho this book is for This book is for anyone involved in the software development life cycle, for those looking to learn about GitHub Actions and what can be accomplished, and for those who want to develop a new skill to help them advance their software development career. If you are new to GitHub and GitHub Actions in general, then this book is for you. Basic knowledge of GitHub as a platform will help you to get the most out of this book.

Automate your software development processes with GitHub Actions, the continuous integration and continuous delivery platform that integrates seamlessly with GitHub. With this practical book, open source author, trainer, and DevOps director Brent Laster explains everything you need to know about using and getting value from GitHub Actions. You'll learn what actions and workflows are and how they can be used, created, and incorporated into your processes to simplify, standardize, and automate your work in GitHub. This book explains the platform, components, use cases, implementation, and integration points of actions, so you can leverage them to provide the functionality and features needed in today's complex pipelines and software development processes. You'll learn how to design and implement automated workflows that respond to common events like pushes, pull requests, and review updates. You'll understand how to use the components of the GitHub Actions platform to gain maximum automation and benefit. With this book, you will: Learn what GitHub Actions are, the various use cases for them, and how to incorporate them into your processes Understand GitHub Actions' structure, syntax, and semantics Automate processes and implement functionality Create your own custom actions with Docker, JavaScript, or shell approachesTroubleshoot and debug workflows that use actions Combine actions with GitHub APIs and other integration options Identify ways to securely implement workflows with GitHub Actions Understand how GitHub Actions compares to other options

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Rust is an exciting new programming language combining the power of C with memory safety, fearless concurrency, and productivity boosters - and what better way to learn than by making games. Each chapter in this book presents hands-on, practical projects ranging from "Hello, World" to building a full dungeon crawler game. With this book, you'll learn game development skills applicable to other engines, including Unity and Unreal. Rust is an exciting programming language combining the power of C with memory safety, fearless concurrency, and productivity boosters. With Rust, you have a shiny new playground where your game ideas can flourish. Each chapter in this book presents hands-on, practical projects that take you on a journey from "Hello, World" to building a full dungeon crawler game. Start by setting up Rust and getting comfortable with your development environment. Learn the language basics with practical examples as you make your own version of Flappy Bird. Discover what it takes to randomly generate dungeons and populate them with monsters as you build a complete dungeon crawl game. Run game systems concurrently for high-performance and fast game-play, while retaining the ability to debug your program. Unleash your creativity with magical items, tougher monsters, and intricate dungeon design. Add layered graphics and polish your game with style. What You Need: A computer running Windows 10, Linux, or Mac OS X.A text editor, such as Visual Studio Code.A video card and drivers capable of running OpenGL 3.2.

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms Keep up with the very latest industry developments, including AI-driven chatbots Book Description Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on ‘grid world’ environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots. What you will learn Understand the DL context of RL and implement complex DL models Learn the foundation of RL: Markov decision processes Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others Discover how to deal with discrete and continuous action spaces in various environments Defeat Atari arcade games using the value iteration method Create your own OpenAI Gym environment to train a stock trading agent Teach your agent to play Connect4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI-driven chatbots Who this book is for Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.

Automate your software development processes with GitHub Actions, the continuous integration and continuous delivery platform that integrates seamlessly with GitHub. With this practical book, open source author, trainer, and DevOps director Brent Laster explains everything you need to know about using and getting value from GitHub Actions. You'll learn what actions and workflows are and how they can be used, created, and incorporated into your processes to simplify, standardize, and automate your work in GitHub. This book explains the platform, components, use cases, implementation, and integration points of actions, so you can leverage them to provide the functionality and features needed in today's complex pipelines and software development processes. You'll learn how to design and implement automated workflows that respond to common events like pushes, pull requests, and review updates. You'll understand how to use the components of the GitHub Actions platform to gain maximum automation and benefit. With this book, you will: Learn what GitHub Actions are, the various use cases for them, and how to incorporate them into your processes Understand GitHub Actions' structure, syntax, and semantics Automate processes and implement functionality Create your own custom actions with Docker, JavaScript, or shell approachesTroubleshoot and debug workflows that use actions Combine actions with GitHub APIs and other integration options Identify ways to securely implement workflows with GitHub Actions Understand how GitHub Actions compares to other options

Explore the full spectrum of GitHub Actions to unlock your team's potential and become a pro in no time Key Features Master GitHub events to foster a self-service mindset Elevate your GitHub Actions knowledge to a whole new level through real-world examples Learn how to integrate with popular cloud-based products within your workflows Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionNavigating GitHub Actions often leaves developers grappling with inefficiencies and collaboration bottlenecks. Mastering GitHub Actions offers solutions to these challenges, ensuring smoother software development. With 16 extensive chapters, this book simplifies GitHub Actions, walking you through its vast capabilities, from team and enterprise features to organization defaults, self-hosted runners, and monitoring tools. You’ll learn how to craft reusable workflows, design bespoke templates, publish actions, incorporate external services, and introduce enhanced security measures. Through hands-on examples, you’ll gain best-practice insights for team-based GitHub Actions workflows and discover strategies for maximizing organization accounts. Whether you’re a software engineer or a DevOps guru, by the end of this book, you'll be adept at amplifying productivity and leveraging automation's might to refine your development process.What you will learn Explore GitHub Actions' features for team and business settings Create reusable workflows, templates, and standardized processes to reduce overhead Get to grips with CI/CD integrations, code quality tools, and communication Understand self-hosted runners for greater control of resources and settings Discover tools to optimize GitHub Actions and manage resources efficiently Work through examples to enhance projects, teamwork, and productivity Who this book is for This book is for developers with a foundation in CI/CD, code quality tools, and team communication keen on exploring GitHub Actions. It’s ideal for DevOps engineers, system administrators, software developers, IT specialists, automation aficionados, and university students focused on software integration and deployment. Those familiar with GitHub's ecosystem will find this content insightful.

Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator Key Features Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks Implement agents to solve simple to complex AI problems Study learning environments and discover how to create your own Book Description Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level. What you will learn Explore intelligent agents and learning environments Understand the basics of RL and deep RL Get started with OpenAI Gym and PyTorch for deep reinforcement learning Discover deep Q learning agents to solve discrete optimal control tasks Create custom learning environments for real-world problems Apply a deep actor-critic agent to drive a car autonomously in CARLA Use the latest learning environments and algorithms to upgrade your intelligent agent development skills Who this book is for If you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.