Github Actions Cookbook
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Github Actions Cookbook
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Author : Michael Kaufmann
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-04-30
Github Actions Cookbook written by Michael Kaufmann and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-30 with Computers categories.
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
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Author : Praveen Palanisamy
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-01-15
Tensorflow 2 Reinforcement Learning Cookbook written by Praveen Palanisamy and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-15 with Computers categories.
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
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Author : Gabor Szauer
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-03-24
Game Physics Cookbook written by Gabor Szauer and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-24 with Computers categories.
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.
Pytorch Computer Vision Cookbook
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Author : Michael Avendi
language : en
Publisher:
Release Date : 2020-03-20
Pytorch Computer Vision Cookbook written by Michael Avendi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-20 with Computers categories.
Discover powerful ways to use deep learning algorithms and solve real-world computer vision problems using Python Key Features Solve the trickiest of problems in computer vision by combining the power of deep learning and neural networks Leverage PyTorch 1.x capabilities to perform image classification, object detection, and more Train and deploy enterprise-grade, deep learning models for computer vision applications Book Description Computer vision techniques play an integral role in helping developers gain a high-level understanding of digital images and videos. With this book, you'll learn how to solve the trickiest problems in computer vision (CV) using the power of deep learning algorithms, and leverage the latest features of PyTorch 1.x to perform a variety of CV tasks. Starting with a quick overview of the PyTorch library and key deep learning concepts, the book then covers common and not-so-common challenges faced while performing image recognition, image segmentation, object detection, image generation, and other tasks. Next, you'll understand how to implement these tasks using various deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). Using a problem-solution approach, you'll learn how to solve any issue you might face while fine-tuning the performance of a model or integrating it into your application. Later, you'll get to grips with scaling your model to handle larger workloads, and implementing best practices for training models efficiently. By the end of this CV book, you'll be proficient in confidently solving many CV related problems using deep learning and PyTorch. What you will learn Develop, train and deploy deep learning algorithms using PyTorch 1.x Understand how to fine-tune and change hyperparameters to train deep learning algorithms Perform various CV tasks such as classification, detection, and segmentation Implement a neural style transfer network based on CNNs and pre-trained models Generate new images and implement adversarial attacks using GANs Implement video classification models based on RNN, LSTM, and 3D-CNN Discover best practices for training and deploying deep learning algorithms for CV applications Who this book is for Computer vision professionals, data scientists, deep learning engineers, and AI developers looking for quick solutions for various computer vision problems will find this book useful. Intermediate-level knowledge of computer vision concepts, along with Python programming experience is required.
React Cookbook
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Author : Carlos Santana Roldán
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-30
React Cookbook written by Carlos Santana Roldán and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-30 with Computers categories.
Over 66 hands-on recipes that cover UI development, animations, component architecture, routing, databases, testing, and debugging with React Key Features Use essential hacks and simple techniques to solve React application development challenges Create native mobile applications for iOS and Android using React Native Learn to write robust tests for your applications using Jest and Enzyme Book Description Today's web demands efficient real-time applications and scalability. If you want to learn to build fast, efficient, and high-performing applications using React 16, this is the book for you. We plunge directly into the heart of all the most important React concepts for you to conquer. Along the way, you’ll learn how to work with the latest ECMAScript features. You'll see the fundamentals of Redux and find out how to implement animations. Then, you’ll learn how to create APIs with Node, Firebase, and GraphQL, and improve the performance of our application with Webpack 4.x. You'll find recipes on implementing server-side rendering, adding unit tests, and debugging. We also cover best practices to deploy a React application to production. Finally, you’ll learn how to create native mobile applications for iOS and Android using React Native. By the end of the book, you'll be saved from a lot of trial and error and developmental headaches, and you’ll be on the road to becoming a React expert. What you will learn Gain the ability to wield complex topics such as Webpack and server-side rendering Implement an API using Node.js, Firebase, and GraphQL Learn to maximize the performance of React applications Create a mobile application using React Native Deploy a React application on Digital Ocean Get to know the best practices when organizing and testing a large React application Who this book is for If you’re a JavaScript developer who wants to build fast, efficient, scalable solutions, then you’re in the right place. Knowledge of React will be an advantage but is not required. Experienced users of React will be able to improve their skills.
Machine Learning For Cybersecurity Cookbook
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Author : Emmanuel Tsukerman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-11-25
Machine Learning For Cybersecurity Cookbook written by Emmanuel Tsukerman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-25 with Computers categories.
Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key FeaturesManage data of varying complexity to protect your system using the Python ecosystemApply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineeringAutomate your daily workflow by addressing various security challenges using the recipes covered in the bookBook Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learnLearn how to build malware classifiers to detect suspicious activitiesApply ML to generate custom malware to pentest your securityUse ML algorithms with complex datasets to implement cybersecurity conceptsCreate neural networks to identify fake videos and imagesSecure your organization from one of the most popular threats – insider threatsDefend against zero-day threats by constructing an anomaly detection systemDetect web vulnerabilities effectively by combining Metasploit and MLUnderstand how to train a model without exposing the training dataWho this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.
Mockito Cookbook
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Author : Marcin Grzejszczak
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-06-24
Mockito Cookbook written by Marcin Grzejszczak and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-24 with Computers categories.
This is a focused guide with lots of practical recipes with presentations of business issues and presentation of the whole test of the system. This book shows the use of Mockito's popular unit testing frameworks such as JUnit, PowerMock, TestNG, and so on. If you are a software developer with no testing experience (especially with Mockito) and you want to start using Mockito in the most efficient way then this book is for you. This book assumes that you have a good knowledge level and understanding of Java-based unit testing frameworks.
R Cookbook
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Author : JD Long
language : en
Publisher: O'Reilly Media
Release Date : 2019-06-21
R Cookbook written by JD Long and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-21 with Computers categories.
Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an intermediate user, this book will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform basic functions Simplify data input and output Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data
Pytorch 1 X Reinforcement Learning Cookbook
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-10-31
Pytorch 1 X Reinforcement Learning Cookbook written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Computers categories.
Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key FeaturesUse PyTorch 1.x to design and build self-learning artificial intelligence (AI) modelsImplement RL algorithms to solve control and optimization challenges faced by data scientists todayApply modern RL libraries to simulate a controlled environment for your projectsBook Description Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems. What you will learnUse Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problemsDevelop a multi-armed bandit algorithm to optimize display advertisingScale up learning and control processes using Deep Q-NetworksSimulate Markov Decision Processes, OpenAI Gym environments, and other common control problemsSelect and build RL models, evaluate their performance, and optimize and deploy themUse policy gradient methods to solve continuous RL problemsWho this book is for Machine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.
Python Feature Engineering Cookbook
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Author : Soledad Galli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-01-22
Python Feature Engineering Cookbook written by Soledad Galli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-22 with Computers categories.
Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries Key FeaturesDiscover solutions for feature generation, feature extraction, and feature selectionUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasetsImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy librariesBook Description Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems. What you will learnSimplify your feature engineering pipelines with powerful Python packagesGet to grips with imputing missing valuesEncode categorical variables with a wide set of techniquesExtract insights from text quickly and effortlesslyDevelop features from transactional data and time series dataDerive new features by combining existing variablesUnderstand how to transform, discretize, and scale your variablesCreate informative variables from date and timeWho this book is for This book is for machine learning professionals, AI engineers, data scientists, and NLP and reinforcement learning engineers who want to optimize and enrich their machine learning models with the best features. Knowledge of machine learning and Python coding will assist you with understanding the concepts covered in this book.