Learning Github Actions

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

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 approaches Troubleshoot 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

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.

Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

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.

Learning Processing, Second Edition, is a friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages. Requiring no previous experience, this book is for the true programming beginner. It teaches the basic building blocks of programming needed to create cutting-edge graphics applications including interactive art, live video processing, and data visualization. Step-by-step examples, thorough explanations, hands-on exercises, and sample code, supports your learning curve. A unique lab-style manual, the book gives graphic and web designers, artists, and illustrators of all stripes a jumpstart on working with the Processing programming environment by providing instruction on the basic principles of the language, followed by careful explanations of select advanced techniques. The book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media. This book is ideal for graphic designers and visual artists without programming background who want to learn programming. It will also appeal to students taking college and graduate courses in interactive media or visual computing, and for self-study. A friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages No previous experience required—this book is for the true programming beginner! Step-by-step examples, thorough explanations, hands-on exercises, and sample code supports your learning curve

Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap