Название: React in Depth (MEAP v5)
Автор: Morten Barklund
Издательство: Manning Publications
Год: 2024
Страниц: 518
Язык: английский
Формат: pdf, epub
Размер: 40.3 MB

Put React to work with this must-have professional collection of advanced React libraries, techniques, and tools. React in Depth is a guide to the advanced React skills used by professional React developers. It focuses on the modern best practices of React development, with full and up-to-date coverage of the latest features and changes to the React ecosystem. This book highlights the techniques companies love to ask about at an interview, and how you can future-proof your career by mastering new React technologies as they emerge. You’ll learn the tools and techniques that are vital to build pro-level apps—and put them into practice with hands-on projects like a goal-focused task manager, expenses tracker, and custom UI library. The React framework boasts a huge ecosystem of libraries and tools that can help you deliver incredible results efficiently. This guide explores exactly what React pros need to know to get the most out of React, from optimizing performance to even creating full-stack web applications. You’ll learn how to put NextJS, Remix, TypeScript, and more in your React toolbox. For web developers familiar with the basics of React.

Название: Deep Learning with PyTorch, Second Edition (MEAP v3)
Автор: Luca Antiga, Eli Stevens, Howard Huang
Издательство: Manning Publications
Год: 2024
Страниц: 273
Язык: английский
Формат: pdf, epub
Размер: 13.7 MB

Everything you need to create neural networks with PyTorch, including Large Language and diffusion models. Deep Learning with PyTorch, Second Edition updates the bestselling original guide with new insights into the transformers architecture and generative AI models. Instantly familiar to anyone who knows PyData tools like NumPy and Scikit-learn, PyTorch simplifies Deep Learning without sacrificing advanced features. In Deep Learning with PyTorch, Second Edition you’ll learn how to create your own neural network and Deep Learning systems and take full advantage of PyTorch’s built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. PyTorch makes it easy to build the powerful neural networks that underpin many modern advances in Artificial Intelligence. This second edition has been thoroughly revised by PyTorch core developer Howard Huang to cover the latest features and applications, including generative AI models. The book is written for developers, students, or even hobbyists who have some prior experience with the Python programming language and want to gain a better understanding of Deep Learning.

Название: Innovative Machine Learning Applications for Cryptography
Автор: J. Anitha Ruth, G.V. Vijayalakshmi, P. Visalakshi
Издательство: IGI Global
Год: 2024
Страниц: 313
Язык: английский
Формат: pdf (true), epub
Размер: 30.9 MB

Data security is paramount in our modern world, and the symbiotic relationship between Machine Learning and cryptography has recently taken center stage. The vulnerability of traditional cryptosystems to human error and evolving cyber threats is a pressing concern. The stakes are higher than ever, and the need for innovative solutions to safeguard sensitive information is undeniable. Innovative Machine Learning Applications for Cryptography emerges as a steadfast resource in this landscape of uncertainty. Machine Learning's prowess in scrutinizing data trends, identifying vulnerabilities, and constructing adaptive analytical models offers a compelling solution. The book explores how Machine Learning can automate the process of constructing analytical models, providing a continuous learning mechanism to protect against an ever-increasing influx of data. This book goes beyond theoretical exploration, and provides a comprehensive resource designed to empower academic scholars, specialists, and students in the fields of cryptography, Machine Learning, and network security. Its broad scope encompasses encryption, algorithms, security, and more unconventional topics like Quantum Cryptography, Biological Cryptography, and Neural Cryptography. By examining data patterns and identifying vulnerabilities, it equips its readers with actionable insights and strategies that can protect organizations from the dire consequences of security breaches.

Название: Генеративное глубокое обучение. Как не мы рисуем картины, пишем романы и музыку, 2-е межд. издание
Автор: Дэвид Фостер
Издательство: Спринт Бук
Год: 2024
Формат: pdf
Страниц: 448
Размер: 18,2 Мб
Язык: русский

Генеративный ИИ — одна из самых обсуждаемых тем в сфере технологий. Пора разобраться с возможностями TensorFlow и Keras, чтобы с легкостью создавать впечатляющие генеративные модели глубокого обучения, включая вариационные автокодировщики (VAE), генеративно-состязательные сети (GAN), трансформеры, нормализующие потоки, модели на основе энергии и диффузионные модели удаления шума. Дэвид Фостер, начинает с основ глубокого обучения и постепенно переходит к передовым архитектурам. Благодаря его советам и подсказкам вы узнаете, как повысить эффективность обучения и творческие возможности ваших моделей. Книга была полностью обновлена и переработана, чтобы соответствовать текущему развитию генеративного обучения.

Название: Learn Generative AI with PyTorch (MEAP v2)
Автор: Mark Liu
Издательство: Manning Publications
Год: 2024
Страниц: 298
Язык: английский
Формат: pdf, epub
Размер: 52.8 MB

Create your own generative AI models for text, images, music, and more! Generative AI tools like ChatGPT, Bard, and DALL-E have transformed the way we work. Learn Generative AI with PyTorch takes you on an incredible hands-on journey through creating and training AI models using Python, the free PyTorch framework and the hardware you already have in your office. Along the way, you’ll master the fundamentals of General Adversarial Networks (GANs), Transformers, Large Language Models (LLMs), variational autoencoders, diffusion models, LangChain, and more! All generative models in this book are deep neural networks. The book starts with a comprehensive Deep Learning project in PyTorch, ideal for those new to the field. Each chapter is carefully structured to build upon the previous one, especially beneficial for readers new to Deep Learning in PyTorch. You'll start by creating basic content like shapes, numbers, and images using Generative Adversarial Networks (GANs) with straightforward architectures. As you progress, the complexity increases, culminating in building advanced models like Transformers.

Название: Optimization Algorithms (MEAP v12)
Автор: Alaa Khamis
Издательство: Manning Publications
Год: 2024
Страниц: 739
Язык: английский
Формат: epub
Размер: 77.6 MB

Solve design, planning, and control problems using modern machine learning and AI techniques. Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. Inside you’ll find a wide range of optimization methods, from deterministic and stochastic derivative-free optimization to nature-inspired search algorithms and machine learning methods. Don’t worry—there’s no complex mathematical notation. You’ll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world.

Название: GitHub Actions in Action (MEAP v2)
Автор: Michael Kaufmann, Rob Bos, Marcel de Vries
Издательство: Manning Publications
Год: 2024
Страниц: 139
Язык: английский
Формат: pdf, epub
Размер: 17.5 MB

Automate your build, test, and deploy pipelines using GitHub Actions! Continuous delivery (CI/CD) pipelines help you automate the software development process and maximize your team’s efficiency. GitHub Actions in Action teaches you to build real-world build, test, and deploy pipelines in GitHub Actions through hands-on labs and projects. GitHub Actions in Action shows you exactly how to implement a secure and reliable continuous delivery process with just the tools available in GitHub—no complex CI/CD frameworks required! You’ll follow an extended example application for selling tickets, taking it all the way from initial build to cloud deployment. The first part of the book introduces the basics of workflows and actions, all illustrated with simple examples. You’ll then move on to the platform’s architecture, security considerations, and in-depth coverage of the workflow runtime. Finally, you’ll learn how to deliver a complete CI/CD pipeline, including compliance and performance and costs optimization. You’ll even learn to create your own actions that you can share in the GitHub marketplace! For software developers and DevOps engineers already working with GitHub and looking to expand to GitHub Actions.

Название: Generative AI for the IT Pro (MEAP v4)
Автор: Chrissy LeMaire, Brandon Abshire
Издательство: Manning Publications
Год: 2024
Страниц: 212
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Automate and accelerate everyday IT tasks using generative AI! Read this book, and you may never write another “after incident” report from scratch again! Generative AI for the IT Pro reveals how you can automate dozens of your daily IT tasks with generative AI—including writing email and reports, setting up a chatbot to field helpdesk requests, evaluating disaster recovery plans, and more. Requirements for this book are simple and even free. While premium AI subscriptions offer more features, starting off with free services like ChatGPT, Google Bard, or Microsoft Copilot will also work. Our primary tool throughout the book is ChatGPT Plus, which we find exceptionally capable and worth the $20/month subscription. Many books deal with the theory, whereas this book deals with practical, hands-on examples that you can immediately apply. In Part I, we lay the groundwork with essential definitions, concepts, and prompts. You’ll get hands-on practice with AI prompt techniques to aid in all AI interactions. In Part II, we’ve packed in relatable examples and strategies, focusing on leveraging AI for tasks like document drafting, communication, and career advancement. As the book progresses to Part III, we cover practical applications in various IT roles: including helpdesk and IT support, database administration, cloud engineering, and everything in between. And for those in management, Part IV offers tailored insights to streamline and enrich your work processes with people and products.

Название: Learn R: As a Language, 2nd Edition
Автор: Pedro J. Aphalo
Издательство: CRC Press
Серия: The R Series
Год: 2024
Страниц: 466
Язык: английский
Формат: pdf (true)
Размер: 11.2 MB

Learning a computer language like R can be either frustrating, fun or boring. Having fun requires challenges that wake up the learner’s curiosity but also provide an emotional reward for overcoming them. The book is designed so that it includes smaller and bigger challenges, in what I call playgrounds, in the hope that all readers will enjoy their path to R fluency. Fluency in the use of a language is a skill that is acquired through practice and exploration. For students and professionals in the biological sciences, humanities and many applied fields, recognizing the parallels between R and natural languages should help them feel at home with R. The approach I use is similar to that of a travel guide, encouraging exploration and describing the available alternatives and how to reach them. The intention is to guide the reader through the R landscape of 2024 and beyond. In R, like in most “rich” languages, there are multiple ways of coding the same operations. I have included code examples that aim to strike a balance between execution speed and readability. One could write equivalent R books using substantially different code examples. Keep this in mind when reading the book and using R. Keep also in mind that it is impossible to remember everything about R, and as a user you will frequently need to consult the documentation, even while doing the exercises in this book. The R language, in a broad sense, is vast because it can be expanded with independently developed packages. Learning to use R mainly consists of learning the basics plus developing the skill of finding your way in R, its documentation and on-line question-and-answer forums.

Название: Accountable and Explainable Methods for Complex Reasoning over Text
Автор: Pepa Atanasova
Издательство: Springer
Год: 2024
Страниц: 208
Язык: английский
Формат: pdf (true)
Размер: 26.7 MB

This thesis presents research that expands the collective knowledge in the areas of accountability and transparency of Machine Learning (ML) models developed for complex reasoning tasks over text. In particular, the presented results facilitate the analysis of the reasons behind the outputs of ML models and assist in detecting and correcting for potential harms. It presents two new methods for accountable ML models; advances the state of the art with methods generating textual explanations that are further improved to be fluent, easy to read, and to contain logically connected multi-chain arguments; and makes substantial contributions in the area of diagnostics for explainability approaches. All results are empirically tested on complex reasoning tasks over text, including fact checking, question answering, and natural language inference. A major concern with Machine Learning (ML) models is their opacity. They are deployed in an increasing number of applications where they often operate as black boxes that do not provide explanations for their predictions. Among others, the potential harms associated with a lack of understanding of the models’ rationales include privacy violations, adversarial manipulations, and unfair discrimination. In Computer Science, the decision-making process of ML models has been studied by developing accountability and transparency methods. Accountability methods, such as adversarial attacks and diagnostic datasets, expose vulnerabilities in ML models that could lead to malicious manipulations or systematic faults in their predictions.