[PDF] Ai Powered Developer eBook

Author : Nathan Crocker
Publisher : Simon and Schuster
Page : 373 pages
File Size : 38,17 MB
Release : 2024-10-01
Category : Computers
ISBN : 1638355711

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Use groundbreaking generative AI tools to increase your productivity, efficiency, and code quality. AI coding tools like ChatGPT and GitHub Copilot are changing the way we write code and build software. AI-Powered Developer reveals the practical best practices you need to deliver reliable results with AI. It cuts through the hype, showcasing real-world examples of how these tools ease and enhance your everyday tasks, and make you more creative. In AI-Powered Developer you’ll discover how to get the most out of AI: • Harness AI to help you design and plan software • Use AI for code generation, debugging, and documentation • Improve your code quality assessments with the help of AI • Articulate complex problems to prompt an AI solution • Develop a continuous learning mindset that keeps you up to date • Adapt your development skills to almost any language AI coding tools give you a smart and reliable junior developer that’s fast and keen to help out with your every task and query. AI-Powered Developer helps you put your new assistant to work. You’ll learn to use AI for everything from writing boilerplate, to testing and quality assessment, managing infrastructure, delivering security, and even assisting with software design. About the technology Using AI tools like Copilot and ChatGPT is like hiring a super-smart and super-fast junior developer eager to take on anything from research to refactoring. Coding with AI can help you work faster, write better applications, and maybe do things that aren’t even possible with your current team. This book will show you how. About the book AI-Powered Developer: Build software with ChatGPT and Copilot teaches you in concrete detail how to maximize the impact of AI coding tools in real-world software development. In it, you’ll walk through a complete application, introducing AI into every step of the workflow. You’ll use ChatGPT and Copilot to generate code and ideas, make predictive suggestions, and develop a self-documenting application. You’ll also learn how AI can help test and explain your code. What's inside • Use AI to design and plan software • Code generation, debugging, and documentation • Improve code quality assessments • Work with unfamiliar programming languages About the reader For intermediate software developers. No AI experience necessary. About the author Nathan B. Crocker is Cofounder and CTO at Checker Corp. The technical editor on this book was Nicolai Nielsen. Table of Contents PART 1 1 Understanding large language models 2 Getting started with large language models PART 2 3 Designing software with ChatGPT 4 Building software with GitHub Copilot 5 Managing data with GitHub Copilot and Copilot Chat PART 3 6 Testing, assessing, and explaining with large language models PART 4 7 Coding infrastructure and managing deployments 8 Secure application development with ChatGPT 9 GPT-ing on the go A Setting up ChatGPT B Setting up GitHub Copilot C Setting up AWS CodeWhisperer

Author : Seth Earley
Publisher : LifeTree Media
Page : 203 pages
File Size : 49,27 MB
Release : 2020-04-28
Category : Business & Economics
ISBN : 1928055524

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Learn how to develop and employ an ontology, the secret weapon for successfully using artificial intelligence to create a powerful competitive advantage in your business. The AI-Powered Enterprise examines two fundamental questions: First, how will the future be different as a result of artificial intelligence? And second, what must companies do to stake their claim on that future? When the Web came along in the mid-90s, it transformed the behavior of customers and remade whole industries. Now, as part of its promise to bring revolutionary change in untold ways to human activity, artificial intelligence—AI—is about to create another complete transformation in how companies create and deliver value to customers. But despite the billions spent so far on bots and other tools, AI continues to stumble. Why can't it magically use all the data organizations generate to make them run faster and better? Because something is missing. AI works only when it understands the soul of the business. An ontology is a holistic digital model of every piece of information that matters to the business, from processes to products to people, and it's what makes the difference between the promise of AI and delivering on that promise. Business leaders who want to catch the AI wave—rather than be crushed by it—need to read The AI-Powered Enterprise. The book is the first to combine a sophisticated explanation of how AI works with a practical approach to applying AI to the problems of business, from customer experience to business operations to product development.

Author : Behram Irani
Publisher :
Page : 0 pages
File Size : 36,76 MB
Release : 2024-08-30
Category : Computers
ISBN : 9781835089149

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Leverage Amazon Q Developer to boost productivity and maximize efficiency by accelerating software development life cycle tasks Key Features: - First book on the market to thoroughly explore all of Amazon Q Developer's features - Gain an understanding of Amazon Q Developer's capabilities across the software development life cycle through real-world examples - Build apps with Amazon Q Developer by auto-generating code in various languages within supported IDEs - Purchase of the print or Kindle book includes a free PDF eBook Book Description: Many developers face the challenge of managing repetitive tasks and maintaining productivity. This book will help you tackle both these challenges with Amazon Q Developer, a generative AI-powered assistant designed to optimize coding and streamline workflows. This book takes you through the setup and customization of Amazon Q Developer, demonstrating how to leverage its capabilities for auto-code generation, code explanation, and transformation across multiple IDEs and programming languages. You'll learn to use Amazon Q Developer to enhance coding experiences, generate accurate code references, and ensure security by scanning for vulnerabilities. The book also shows you how to use Amazon Q Developer for AWS-related tasks, including solution building, applying architecture best practices, and troubleshooting errors. Each chapter provides practical insights and step-by-step guidance to help you fully integrate this powerful tool into your development process. You'll get to grips with effortless code implementation, explanation, transformation, and documentation, helping you create applications faster and improve your development experience. By the end of this book, you'll have mastered Amazon Q Developer to accelerate your software development lifecycle, improve code quality, and build applications faster and more efficiently. What You Will Learn: - Understand the importance of generative AI-powered assistants in developers' daily work - Enable Amazon Q Developer for IDEs and with AWS services to leverage code suggestions - Customize Amazon Q Developer to align with organizational coding standards - Utilize Amazon Q Developer for code explanation, transformation, and feature development - Understand code references and scan for code security issues using Amazon Q Developer - Accelerate building solutions and troubleshooting errors on AWS Who this book is for: This book is for coders, software developers, application builders, data engineers, and technical resources using AWS services looking to leverage Amazon Q Developer's features to enhance productivity and accelerate business outcomes. Basic coding skills are needed to understand the concepts covered in this book. Table of Contents - Introduction to Generative AI-Powered Assistants - Introducing and Setting Up Amazon Q Developer - Understanding Auto-Code Generation Techniques - Boost Coding Efficiency for Python and Java with Auto-Code Generation - Boost Coding Efficiency for C and C++ with Auto-Code Generation - Boost Coding Efficiency for JavaScript and PHP with Auto-Code Generation - Boost Coding Efficiency for SQL with Auto-Code Generation - Boost Coding Efficiency for Command Line and Shell Script with Auto-Code Generation - Boost Coding Efficiency for JSON, YAML, and HCL with Auto-Code Generation - Customizing Code Recommendations - Understanding Code References - Simplifying Code Explanation, Optimization, Transformation, and Feature Development - Simplifying Scanning and Fixing Security Vulnerabilities in Code - Accelerate Data Engineering on AWS (N.B. Please use the Read Sample option to see further chapters)

Author : Noah Gift
Publisher : Addison-Wesley Professional
Page : 720 pages
File Size : 17,78 MB
Release : 2018-07-12
Category : Computers
ISBN : 0134863917

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Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Author : Behram Irani
Publisher : Packt Publishing Ltd
Page : 416 pages
File Size : 20,24 MB
Release : 2024-08-30
Category : Computers
ISBN : 1835081207

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Leverage Amazon Q Developer to boost productivity and maximize efficiency by accelerating software development life cycle tasks Key Features First book on the market to thoroughly explore all of Amazon Q Developer’s features Gain an understanding of Amazon Q Developer's capabilities across the software development life cycle through real-world examples Build apps with Amazon Q Developer by auto-generating code in various languages within supported IDEs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany developers face the challenge of managing repetitive tasks and maintaining productivity. This book will help you tackle both these challenges with Amazon Q Developer, a generative AI-powered assistant designed to optimize coding and streamline workflows. This book takes you through the setup and customization of Amazon Q Developer, demonstrating how to leverage its capabilities for auto-code generation, code explanation, and transformation across multiple IDEs and programming languages. You'll learn to use Amazon Q Developer to enhance coding experiences, generate accurate code references, and ensure security by scanning for vulnerabilities. The book also shows you how to use Amazon Q Developer for AWS-related tasks, including solution building, applying architecture best practices, and troubleshooting errors. Each chapter provides practical insights and step-by-step guidance to help you fully integrate this powerful tool into your development process. You’ll get to grips with effortless code implementation, explanation, transformation, and documentation, helping you create applications faster and improve your development experience. By the end of this book, you’ll have mastered Amazon Q Developer to accelerate your software development lifecycle, improve code quality, and build applications faster and more efficiently.What you will learn Understand the importance of generative AI-powered assistants in developers' daily work Enable Amazon Q Developer for IDEs and with AWS services to leverage code suggestions Customize Amazon Q Developer to align with organizational coding standards Utilize Amazon Q Developer for code explanation, transformation, and feature development Understand code references and scan for code security issues using Amazon Q Developer Accelerate building solutions and troubleshooting errors on AWS Who this book is for This book is for coders, software developers, application builders, data engineers, and technical resources using AWS services looking to leverage Amazon Q Developer's features to enhance productivity and accelerate business outcomes. Basic coding skills are needed to understand the concepts covered in this book.

Author : Edward R DeForest
Publisher : Independently Published
Page : 0 pages
File Size : 45,71 MB
Release : 2024-03-17
Category : Computers
ISBN :

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Feeling overwhelmed by repetitive coding tasks? Struggling to keep up with the ever-evolving landscape of software development? In today's fast-paced world, developers need every edge they can get. Gemini AI for Developers: The Ultimate Guide to Boosting Productivity 10x is your key to unlocking the power of AI-powered development assistance. This comprehensive guide will equip you with the knowledge and skills to: Master Gemini AI's core functionalities and streamline your development workflow. Leverage code completion, code search, error checking, and refactoring suggestions to write cleaner, more efficient code. Optimize your development environment for seamless integration with Gemini AI. Troubleshoot common issues and access valuable resources within the Gemini AI community. Explore emerging trends in AI-powered development and prepare for the future of coding. Stop spending hours on repetitive tasks! With Gemini AI as your co-pilot, you can focus on the creative aspects of development and bring your ideas to life faster than ever before. Are you ready to: Boost your development productivity by 10x? Write higher quality code with fewer errors? Stay ahead of the curve in the exciting world of AI-powered development? Then dive into Gemini AI for Developers: The Ultimate Guide to Boosting Productivity 10x and unlock your full potential as a developer!

Author : StoryBuddiesPlay
Publisher : StoryBuddiesPlay
Page : 59 pages
File Size : 26,20 MB
Release : 2024-05-03
Category : Computers
ISBN :

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Unleash the transformative potential of AI in software development with this comprehensive guide to Copilot and its impact on the future of the field. Discover how AI-powered tools are revolutionizing workflows, boosting developer productivity, and democratizing access to software creation. Dive into the technical aspects of Copilot, exploring its machine learning and natural language processing capabilities. Understand how AI assists with code generation, autocompletion, and context-aware suggestions, leading to significant efficiency gains. This guide delves into the broader implications of AI, analyzing its potential to: Reduce the time spent on repetitive tasks: Automate boilerplate code, streamline debugging, and enhance code navigation, freeing developers for higher-level problem-solving. Empower new developers and foster a more diverse talent pool: AI-powered tools can lower the barrier to entry, enabling faster upskilling and specialization within the field. Shift the focus towards creativity and problem-solving: As AI handles routine tasks, developers can dedicate their energy to strategic thinking, design, and innovative solutions. Explore the evolving landscape of software development jobs, where AI necessitates a reevaluation of roles and skillsets. Learn how AI is creating new opportunities for collaboration and specialization, while emphasizing the continued importance of human oversight and critical thinking. Addressing the crucial ethical considerations of AI development, this guide emphasizes the need for responsible practices and mitigating potential biases within AI models. Discover strategies for ensuring fairness, security, and transparency throughout the software development process. This comprehensive exploration of AI in software development equips you with the knowledge to navigate the future of the field. Prepare to collaborate with AI tools, embrace continuous learning, and shape a future of software creation driven by human ingenuity and technological advancements.

Author : Kevin Ferguson
Publisher : Simon and Schuster
Page : 611 pages
File Size : 24,56 MB
Release : 2019-01-06
Category : Computers
ISBN : 1638354014

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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

Author : Laurence Moroney
Publisher : O'Reilly Media
Page : 393 pages
File Size : 45,68 MB
Release : 2020-10-01
Category : Computers
ISBN : 1492078166

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If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

Author : Laurence Moroney
Publisher : "O'Reilly Media, Inc."
Page : 328 pages
File Size : 45,40 MB
Release : 2021-08-12
Category : Computers
ISBN : 1098101707

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AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it's essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating and running models on popular mobile platforms such as iOS and Android. Laurence Moroney, lead AI advocate at Google, offers an introduction to machine learning techniques and tools, then walks you through writing Android and iOS apps powered by common ML models like computer vision and text recognition, using tools such as ML Kit, TensorFlow Lite, and Core ML. If you're a mobile developer, this book will help you take advantage of the ML revolution today. Explore the options for implementing ML and AI on mobile devices Create ML models for iOS and Android Write ML Kit and TensorFlow Lite apps for iOS and Android, and Core ML/Create ML apps for iOS Choose the best techniques and tools for your use case, such as cloud-based versus on-device inference and high-level versus low-level APIs Learn privacy and ethics best practices for ML on devices