Learning Github Copilot

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Learning Github Copilot

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Author : Brent Laster
language : en
Publisher:
Release Date : 2025-09-30

Learning Github Copilot written by Brent Laster and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-30 with Computers categories.

Harness the power of generative AI in your coding with GitHub Copilot. In this practical guide, author Brent Laster explains what Copilot is, how it works, and what it can do for you. You'll learn how to leverage AI to automate and simplify development, testing, documentation, and more. Software professionals in all roles will find the information needed to supercharge your productivity. Learning GitHub Copilot shows DevOps engineers, software developers, and database administrators how to make the most of the tool's code completion and generation capabilities. You'll understand how to take advantage of the tool's chat interface to get rich, detailed responses. Suitable for all skill levels--no matter what coding language or framework you use--Learning GitHub Copilot provides the knowledge you need to fully utilize Copilot. Learn how GitHub Copilot leverages GenAI and what it can do Understand when and how to use Copilot's separate inline and chat interfaces Explore ways to use Copilot for creating, testing, documenting, explaining, and fixing code Learn how to use Copilot for testing frameworks, SQL generation, and Kubernetes manifests Understand how Copilot integrates with IDEs and GitHub Discover the key strategies, tips, and tricks to make the most of Copilot's capabilities

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Author :
language : en
Publisher: "O'Reilly Media, Inc."
Release Date :

written by and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Deep Learning And The Game Of Go

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Author : Kevin Ferguson
language : en
Publisher: Simon and Schuster
Release Date : 2019-01-06

Deep Learning And The Game Of Go written by Kevin Ferguson and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-06 with Computers categories.

Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning

Machine Learning With Pytorch And Scikit Learn

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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-02-25

Machine Learning With Pytorch And Scikit Learn written by Sebastian Raschka 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 2022-02-25 with Computers categories.

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Learning Continuous Integration With Jenkins

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Author : Nikhil Pathania
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31

Learning Continuous Integration With Jenkins written by Nikhil Pathania 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 2024-01-31 with Computers categories.

Integrate Jenkins, Kubernetes, and more on cloud into a robust, GitOps-driven CI/CD system, leveraging JCasC, IaC, and AI for a streamlined software delivery process Key Features Follow the construction of a Jenkins CI/CD pipeline start to finish through a real-world example Construct a continuous deployment (CD) pipeline in Jenkins using GitOps principles and integration with Argo CD Craft and optimize your CI pipeline code with ChatGPT and GitHub Copilot Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis updated edition of Learning Continuous Integration with Jenkins is your one-stop guide to implementing CI/CD with Jenkins, addressing crucial technologies such as cloud computing, containerization, Infrastructure as Code, and GitOps. Tailored to both beginners and seasoned developers, the book provides a practical path to mastering a production-grade, secure, resilient, and cost-effective CI/CD setup. Starting with a detailed introduction to the fundamental principles of CI, this book systematically takes you through setting up a CI environment using Jenkins and other pivotal DevOps tools within the CI/CD ecosystem. You’ll learn to write pipeline code with AI assistance and craft your own CI pipeline. With the help of hands-on tutorials, you’ll gain a profound understanding of the CI process and Jenkins’ robust capabilities. Additionally, the book teaches you how to expand your CI pipeline with automated testing and deployment, setting the stage for continuous deployment. To help you through the complete software delivery process, this book also covers methods to ensure that your CI/CD setup is maintainable across teams, secure, and performs optimally. By the end of the book, you’ll have become an expert in implementing and optimizing CI/CD setups across diverse teams.What you will learn Understand CI with the Golden Circle theory Deploy Jenkins on the cloud using Helm charts and Jenkins Configuration as Code (JCasC) Implement optimal security practices to ensure Jenkins operates securely Extend Jenkins for CI by integrating with SonarQube, GitHub, and Artifactory Scale Jenkins using containers and the cloud for optimal performance Master Jenkins declarative syntax to enrich your pipeline coding vocabulary Enhance security and improve pipeline code within your CI/CD process using best practices Who this book is for This book is for a diverse audience, from university students studying Agile software development to seasoned developers, testers, release engineers, and project managers. If you’re already using Jenkins for CI, this book will assist you in elevating your projects to CD. Whether you’re new to the concepts of Agile, CI, and CD, or a DevOps engineer seeking advanced insights into JCasC, IaC, and Azure, this book will equip you with the tools to harness Jenkins for improved productivity and streamlined deliveries in the cloud.

Artificial Intelligence Is Here To Stay You Better Learn It Now

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Author : Patience Fuzane
language : en
Publisher: Mr. Patience Fuzane
Release Date :

Artificial Intelligence Is Here To Stay You Better Learn It Now written by Patience Fuzane and has been published by Mr. Patience Fuzane this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

Artificial Intelligence (AI) is no longer just a futuristic concept; it is here, and it is transforming the way we live, work, and interact. If you are not actively harnessing the power of AI, you risk being left behind in an increasingly competitive and fast-paced world. Just like the calculator revolutionized the way we perform basic arithmetic, AI is now stepping in to revolutionize a wide array of fields—academic, business, and social. When the calculator was first introduced, many feared it would take away jobs and diminish mental faculties. But over time, we have come to view it as an indispensable tool, one that saves us time and allows us to focus on more complex tasks. In much the same way, AI is poised to become an essential part of our daily lives, yet its potential goes far beyond what a calculator ever offered. From automating mundane tasks to solving complex problems, AI has the power to revolutionize virtually every aspect of our existence. As we move forward, it is crucial to embrace these technological advancements and learn how to effectively incorporate them into our routines. This book is a comprehensive guide to understanding and utilizing some of the most widely used AI tools available today. Across the following chapters, I will take you on a deep dive into AI solutions across various categories, including generative AI, productivity tools, coding assistants, design tools, data analysis, and more. Whether you're a student, a business owner, or simply someone curious about how AI can improve your life, this book is designed to help you navigate and leverage these powerful technologies to meet your academic, professional, and social needs.

Deep Learning With Pytorch

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Author : Luca Pietro Giovanni Antiga
language : en
Publisher: Simon and Schuster
Release Date : 2020-07-01

Deep Learning With Pytorch written by Luca Pietro Giovanni Antiga and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Computers categories.

“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

Structure And Interpretation Of Computer Programs Second Edition

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Author : Harold Abelson
language : en
Publisher: MIT Press
Release Date : 1996-07-25

Structure And Interpretation Of Computer Programs Second Edition written by Harold Abelson and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-07-25 with Computers categories.

Structure and Interpretation of Computer Programs has had a dramatic impact on computer science curricula over the past decade. This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

Practicing Trustworthy Machine Learning

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Author : Yada Pruksachatkun
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-01-03

Practicing Trustworthy Machine Learning written by Yada Pruksachatkun and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-03 with Business & Economics categories.

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention

Deep Learning With Python

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Author : Francois Chollet
language : en
Publisher: Simon and Schuster
Release Date : 2017-11-30

Deep Learning With Python written by Francois Chollet and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-30 with Computers categories.

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance