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Название: Hands-On Generative AI with Transformers and Diffusion Models (6th Early Release)
Автор: Omar Sanseviero, Pedro Cuenca, Apolinario Passos, Jonathan Whitaker
Издательство: O’Reilly Media, Inc.
Год: 2024-09-06
Страниц: 419
Язык: английский
Формат: epub
Размер: 28.7 MB

Learn how to use generative media techniques with AI to create novel images or music in this practical, hands-on guide. Data scientists and software engineers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to your needs, and how to combine existing building blocks to create new models and creative applications in different domains. This book introduces theoretical concepts in an intuitive way, with extensive code samples and illustrations that you can run on services such as Google Colaboratory, Kaggle, or Hugging Face Spaces with minimal setup. You'll learn how to use open source libraries such as Transformers and Diffusers, conduct code exploration, and study several existing projects to help guide your work. This book assumes that you are comfortable programming in Python and have a foundational understanding of what Machine Learning is, including basic usage of frameworks like PyTorch or TensorFlow.

Название: Machine Learning and Metaheuristic Computation
Автор: Erik Cuevas, Jorge Galvez, Omar Avalos, Fernando Wario
Издательство: Wiley-IEEE Press
Год: 2025
Страниц: 418
Язык: английский
Формат: pdf, epub (true)
Размер: 30.3 MB

Learn to bridge the gap between Machine Learning and metaheuristic methods to solve problems in optimization approaches. Few areas of technology have greater potential to revolutionize the globe than Artificial Intelligence. Two key areas of Artificial Intelligence, Machine Learning and metaheuristic computation, have an enormous range of individual and combined applications in Computer Science and technology. To date, these two complementary paradigms have not always been treated together, despite the potential of a combined approach which maximizes the utility and minimizes the drawbacks of both. Machine Learning and Metaheuristic Computation offers an introduction to both of these approaches and their joint applications. Both a reference text and a course, it is built around the popular Python programming language to maximize utility. It guides the reader gradually from an initial understanding of these crucial methods to an advanced understanding of cutting-edge Artificial Intelligence tools. For enthusiasts and practitioners in Machine Learning who may not be well-versed in metaheuristic computation, this book is an essential resource.

Название: Тестирование программного обеспечения: контекстно ориентированный подход
Автор: Кейнер Кем, Бах Джеймс, Петтикорд Брет
Издательство: Питер
Год: 2025
Формат: pdf
Страниц: 352
Размер: 27,3 Мб
Язык: русский

Хорошими тестировщиками не рождаются — ими становятся благодаря упорному труду и постоянному общению. На этом пути таится множество ловушек, способных сорвать самые смелые планы и привести к отставанию проектов от графика. Кем Кейнер, Джеймс Бах и Брет Петтикорд очень хорошо об этом знают. За их плечами более 50 лет опыта, и они понимают, что необходимо для достижения успеха в тестировании. Они собрали 293 проверенных совета, которые вы можете использовать в своих проектах. Каждый урок начинается с утверждения, относящегося к тестированию программного обеспечения, за которым следует объяснение или пример, показывающий, как, когда и почему применяется этот урок.

Название: Java Essentials For Dummies
Автор: Doug Lowe, Paul McFedries
Издательство: For Dummies
Год: 2025
Страниц: 211
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Swiftly brush up on the foundations of Java programming. Java Essentials For Dummies is a reliable and succinct reference on the core components of Java—the multifaceted general-purpose language used for desktop, mobile, and web applications. This straightforward book gets right to the point—eliminating review material and wordy explanations—so you get what you need, fast. This book contains all the basic information you need to know to get going with Java programming, starting with writing statements and using variables and ending with techniques for using Java features such as arrays. Along the way, you find plenty of not-so-basic information about working with classes and objects, handling exceptions, and working from the command line.

Название: Data Science Essentials For Dummies
Автор: Lillian Pierson
Издательство: For Dummies
Год: 2025
Страниц: 195
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Feel confident navigating the fundamentals of Data Science. Data Science Essentials For Dummies is a quick reference on the core concepts of the exploding and in-demand Data Science field, which involves data collection and working on dataset cleaning, processing, and visualization. This direct and accessible resource helps you brush up on key topics and is right to the point—eliminating review material, wordy explanations, and fluff—so you get what you need, fast. Although popular programming languages like Java and C++ are good for developing stand-alone desktop applications, Python’s versatility makes it an ideal programming language for processing, analyzing, and visualizing data. For this reason, Python has earned a reputation of excellence in the Data Science field, where it has been widely adopted over the past decade. Python’s status as one of the more popular programming languages out there can be linked to the fact that it’s relatively easy to learn and it allows users to accomplish several tasks using just a few lines of code.

Название: Mastering ARKit: Apple's Augmented Reality App Development Platform, 2nd Edition
Автор: Jayven Nhan
Издательство: Apress
Год: 2024
Страниц: 347
Язык: английский
Формат: pdf
Размер: 10.7 MB

Embark on a journey to build an augmented reality world. This book puts theory into practical application by building unique augmented reality apps specific to each chapter’s topic. You’ll learn to engineer successful, well-designed, and comprehensive augmented reality applications for iOS devices. This updated edition has been thoroughly revised to cover the latest advancements in iOS 17 and Xcode 15, equipping you with the knowledge and tools to build cutting-edge augmented reality experiences. Starting with the foundation of augmented reality on Apple platforms, you will understand the building blocks and inner workings of the technologies that power augmented reality. Then, delve into the practicalities of building AR apps using ARKit, SceneKit, SpriteKit, RealityKit, and integrating with SwiftUI and advanced features like ReplayKit for recording experiences. For iOS developers who want to expand their knowledge of ARKit. Ideal for those with a solid foundation in Swift and familiarity with Xcode, seeking to explore the expansive capabilities of augmented reality on Apple devices.

Название: Effective Python: 125 Specific Ways to Write Better Python, 3rd Edition (Final Release)
Автор: Brett Slatkin
Издательство: Addison-Wesley Professional/Pearson Education
Год: 2025
Страниц: 672
Язык: английский
Формат: epub (true)
Размер: 82.2 MB

Master the art of Python programming with 125 actionable best practices to write more efficient, readable, and maintainable code. Python is a versatile and powerful language, but leveraging its full potential requires more than just knowing the syntax. Effective Python: 125 Specific Ways to Write Better Python, 3rd Edition is your comprehensive guide to mastering Python's unique strengths and avoiding its hidden pitfalls. This updated edition builds on the acclaimed second edition, expanding from 90 to 125 best practices that are essential for writing high-quality Python code. Drawing on years of experience at Google, Brett Slatkin offers clear, concise, and practical advice for both new and experienced Python developers. Each item in the book provides insight into the "Pythonic" way of programming, helping you understand how to write code that is not only effective but also elegant and maintainable. Whether you're building web applications, analyzing data, writing automation scripts, or training AI models, this book will equip you with the skills to make a significant impact using Python.

Название: MATLAB Deep Learning Toolbox User’s Guide (R2024b)
Автор: Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth
Издательство: The MathWorks, Inc.
Год: September 2024
Страниц: 5466
Язык: английский
Формат: pdf (true)
Размер: 86.1 MB

Deep Learning (DL) is a branch of Machine Learning (ML) that teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. Deep Learning is especially suited for image recognition, which is important for solving problems such as facial recognition, motion detection, and many advanced driver assistance technologies such as autonomous driving, lane detection, pedestrian detection, and autonomous parking. Deep Learning Toolbox provides simple MATLAB commands for creating and interconnecting the layers of a deep neural network. Examples and pretrained networks make it easy to use MATLAB for deep learning, even without knowledge of advanced computer vision algorithms or neural networks. Deep Learning Toolbox provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can exchange models with TensorFlow and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.

Название: Mastering Algorithm in Python
Автор: Ed Norex
Издательство: HiTeX Press
Год: 2024
Страниц: 556
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Master the art of solving complex problems with "Mastering Algorithm in Python," your comprehensive guide to understanding and applying algorithms using one of the most versatile programming languages. Whether you're a beginner eager to dive into the world of Computer Science or a seasoned professional looking to sharpen your skills, this book covers everything from fundamental concepts to advanced techniques. Unlock the secrets of data structures, delve into the intricacies of searching and sorting algorithms, navigate through the complexities of graph algorithms, and conquer challenges with dynamic programming, greedy algorithms, divide and conquer strategies, and backtracking algorithms. Elevate your expertise further as you explore advanced topics including Machine Learning and graphical models, all illustrated through clear, practical Python examples.

Название: Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach
Автор: Courage Kamusoko
Издательство: CRC Press
Год: 2025
Страниц: 280
Язык: английский
Формат: pdf (true), epub
Размер: 44.9 MB

Explainable Machine Learning (XML), a subfield of Artificial Intelligence (AI), is focused on making complex AI models understandable to humans. This book highlights and explains the details of Machine Learning models used in geospatial data analysis. It demonstrates the need for a data-centric, explainable Machine Learning approach to obtain new insights from geospatial data. It presents the opportunities, challenges, and gaps in the Machine Learning and Deep Learning approaches for geospatial data analysis and how they are applied to solve various environmental problems in land cover changes and in modeling forest canopy height and aboveground biomass density. The author also includes guidelines and code scripts (R, Python) valuable for practical readers.