Ai Powered Developer
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| Author | : Nathan Crocker |
| Publisher | : Simon and Schuster |
| Total Pages | : 373 |
| Release | : 2024-10-01 |
| Genre | : Computers |
| ISBN | : 1638355711 |
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 | : Behram Irani |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 416 |
| Release | : 2024-08-30 |
| Genre | : Computers |
| ISBN | : 1835081207 |
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 | : Peter Elger |
| Publisher | : Simon and Schuster |
| Total Pages | : 326 |
| Release | : 2020-09-05 |
| Genre | : Computers |
| ISBN | : 1638350434 |
AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory. Instead, you’ll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide! Summary Companies everywhere are moving everyday business processes over to the cloud, and AI is increasingly being given the reins in these tasks. As this massive digital transformation continues, the combination of serverless computing and AI promises to become the de facto standard for business-to-consumer platform development—and developers who can design, develop, implement, and maintain these systems will be in high demand! AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory. Instead, you’ll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Cloud-based AI services can automate a variety of labor intensive business tasks in areas such as customer service, data analysis, and financial reporting. The secret is taking advantage of pre-built tools like Amazon Rekognition for image analysis or AWS Comprehend for natural language processing. That way, there’s no need to build expensive custom software. Artificial Intelligence (AI), a machine’s ability to learn and make predictions based on patterns it identifies, is already being leveraged by businesses around the world in areas like targeted product recommendations, financial forecasting and resource planning, customer service chatbots, healthcare diagnostics, data security, and more. With the exciting combination of serverless computing and AI, software developers now have enormous power to improve their businesses’ existing systems and rapidly deploy new AI-enabled platforms. And to get on this fast-moving train, you don’t have to invest loads of time and effort in becoming a data scientist or AI expert, thanks to cloud platforms and the readily available off-the-shelf cloud-based AI services! About the book AI as a Service is a fast-paced guide to harnessing the power of cloud-based solutions. You’ll learn to build real-world apps—such as chatbots and text-to-speech services—by stitching together cloud components. Work your way from small projects to large data-intensive applications. What's inside - Apply cloud AI services to existing platforms - Design and build scalable data pipelines - Debug and troubleshoot AI services - Start fast with serverless templates About the reader For software developers familiar with cloud basics. About the author Peter Elger and Eóin Shanaghy are founders and CEO/CTO of fourTheorem, a software solutions company providing expertise on architecture, DevOps, and machine learning. Table of Contents PART 1 - FIRST STEPS 1 A tale of two technologies 2 Building a serverless image recognition system, part 1 3 Building a serverless image recognition system, part 2 PART 2 - TOOLS OF THE TRADE 4 Building and securing a web application the serverless way 5 Adding AI interfaces to a web application 6 How to be effective with AI as a Service 7 Applying AI to existing platforms PART 3 - BRINGING IT ALL TOGETHER 8 Gathering data at scale for real-world AI 9 Extracting value from large data sets with AI
| Author | : Ian Millington |
| Publisher | : CRC Press |
| Total Pages | : 85 |
| Release | : 2021-11-15 |
| Genre | : Computers |
| ISBN | : 1000475514 |
What is artificial intelligence? How is artificial intelligence used in game development? Game development lives in its own technical world. It has its own idioms, skills, and challenges. That’s one of the reasons games are so much fun to work on. Each game has its own rules, its own aesthetic, and its own trade-offs, and the hardware it will run on keeps changing. AI for Games is designed to help you understand one element of game development: artificial intelligence (AI).
| Author | : Andrew Ferlitsch |
| Publisher | : Simon and Schuster |
| Total Pages | : 755 |
| Release | : 2021-10-12 |
| Genre | : Computers |
| ISBN | : 163835667X |
Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: Internal functioning of modern convolutional neural networks Procedural reuse design pattern for CNN architectures Models for mobile and IoT devices Assembling large-scale model deployments Optimizing hyperparameter tuning Migrating a model to a production environment The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example. About the book Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects. What's inside Modern convolutional neural networks Design pattern for CNN architectures Models for mobile and IoT devices Large-scale model deployments Examples for computer vision About the reader For machine learning engineers familiar with Python and deep learning. About the author Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations. Table of Contents PART 1 DEEP LEARNING FUNDAMENTALS 1 Designing modern machine learning 2 Deep neural networks 3 Convolutional and residual neural networks 4 Training fundamentals PART 2 BASIC DESIGN PATTERN 5 Procedural design pattern 6 Wide convolutional neural networks 7 Alternative connectivity patterns 8 Mobile convolutional neural networks 9 Autoencoders PART 3 WORKING WITH PIPELINES 10 Hyperparameter tuning 11 Transfer learning 12 Data distributions 13 Data pipeline 14 Training and deployment pipeline
| Author | : Ben Auffarth |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 459 |
| Release | : 2020-10-30 |
| Genre | : Computers |
| ISBN | : 1789137969 |
Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook Description Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production. What you will learnImplement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practiceWho this book is for This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.
| Author | : Yuki Hattori |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 284 |
| Release | : 2024-04-19 |
| Genre | : Computers |
| ISBN | : 1835468683 |
Unlock the full potential of your team with Git mastery, seamless DevOps workflows, and the power of AI integration Key Features Gain a comprehensive understanding of Git, GitHub, and DevOps with practical implementation tips Embark on a holistic exploration of DevOps workflows, scaling, DevSecOps, and GitHub Copilot Discover the best practices for optimizing processes and team productivity Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGit and GitHub are absolutely crucial for DevOps, playing a multifaceted role in streamlining the software development lifecycle and enabling smoother collaboration between development and operations teams. DevOps Unleashed with Git and GitHub enables you to harness the power of Git and GitHub to streamline workflows, drive collaboration, and fuel innovation. Authored by an expert from GitHub, the book starts by guiding you through Git fundamentals and delving into DevOps and the developer experience. As you progress, you’ll understand how to leverage GitHub's collaboration and automation features, and even use GitHub Copilot for enhanced productivity. You'll also learn how to bridge the DevOps gap, maintain code quality, and implement robust security measures. Additionally, hands-on exercises will equip you to elevate your developer experience, foster teamwork, and drive innovation at the speed of DevOps. By the end of this DevOps book, you’ll have mastered the Git fundamentals, conquered collaboration challenges, and unleashed the power of GitHub as you transform your DevOps workflows.What you will learn Master the fundamentals of Git and GitHub Unlock DevOps principles that drive automation, continuous integration and continuous deployment (CI/ CD), and monitoring Facilitate seamless cross-team collaboration Boost productivity using GitHub Actions Measure and improve development velocity Leverage the GitHub Copilot AI tool to elevate your developer experience Who this book is for If you’re aiming to enhance collaboration, productivity, and DevOps practices to enrich your development experience, this book is for you. Novice DevOps engineers will be able resolve their doubts surrounding Git and GitHub errors, while IT admins and system engineers will be able to effortlessly embrace DevOps principles with pragmatic insights. For infrastructure engineers looking to delve into cloud-based collaboration and optimal management practices, this book provides valuable knowledge to facilitate a seamless transition into the DevOps landscape.
| Author | : Andy Pandharikar |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 256 |
| Release | : 2022-01-28 |
| Genre | : Computers |
| ISBN | : 1803234075 |
Learn how to use artificial intelligence for product and service innovation, including the diverse use cases of Commerce.AI Key FeaturesLearn how to integrate data and AI in your innovation workflowsUnlock insights into how various industries are using AI for innovationApply your knowledge to real innovation use cases like product strategy and market intelligenceBook Description Commerce.AI is a suite of artificial intelligence (AI) tools, trained on over a trillion data points, to help businesses build next-gen products and services. If you want to be the best business on the block, using AI is a must. Developers and analysts working with AI will be able to put their knowledge to work with this practical guide. You'll begin by learning the core themes of new product and service innovation, including how to identify market opportunities, come up with ideas, and predict trends. With plenty of use cases as reference, you'll learn how to apply AI for innovation, both programmatically and with Commerce.AI. You'll also find out how to analyze product and service data with tools such as GPT-J, Python pandas, Prophet, and TextBlob. As you progress, you'll explore the evolution of commerce in AI, including how top businesses today are using AI. You'll learn how Commerce.AI merges machine learning, product expertise, and big data to help businesses make more accurate decisions. Finally, you'll use the Commerce.AI suite for product ideation and analyzing market trends. By the end of this artificial intelligence book, you'll be able to strategize new product opportunities by using AI, and also have an understanding of how to use Commerce.AI for product ideation, trend analysis, and predictions. What you will learnFind out how machine learning can help you identify new market opportunitiesUnderstand how to use consumer data to create new products and servicesUse state-of-the-art AI frameworks and tools for data analysisLaunch, track, and improve products and services with AIRise above the competition with unparalleled insights from AITurn customer touchpoints into business winsGenerate high-conversion product and service copyWho this book is for This AI book is for AI developers, data scientists, data product managers, analysts, and consumer insights professionals. The book will guide you through the process of product and service innovation, no matter your pre-existing skillset.
| Author | : StoryBuddiesPlay |
| Publisher | : StoryBuddiesPlay |
| Total Pages | : 59 |
| Release | : 2024-05-03 |
| Genre | : Computers |
| ISBN | : |
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 | : Raoul-Gabriel Urma |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 214 |
| Release | : 2019-12-02 |
| Genre | : Computers |
| ISBN | : 1491967129 |
Explore the latest Java-based software development techniques and methodologies through the project-based approach in this practical guide. Unlike books that use abstract examples and lots of theory, Real-World Software Development shows you how to develop several relevant projects while learning best practices along the way. With this engaging approach, junior developers capable of writing basic Java code will learn about state-of-the-art software development practices for building modern, robust and maintainable Java software. You’ll work with many different software development topics that are often excluded from software develop how-to references. Featuring real-world examples, this book teaches you techniques and methodologies for functional programming, automated testing, security, architecture, and distributed systems.
