Google Jax Essentials
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Google Jax Essentials
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Author : Mei Wong
language : en
Publisher: GitforGits
Release Date : 2023-05-31
Google Jax Essentials written by Mei Wong and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Computers categories.
"Google JAX Essentials" is a comprehensive guide designed for machine learning and deep learning professionals aiming to leverage the power and capabilities of Google's JAX library in their projects. Over the course of eight chapters, this book takes the reader from understanding the challenges of deep learning and numerical computations in the existing frameworks to the essentials of Google JAX, its functionalities, and how to leverage it in real-world machine learning and deep learning projects. The book starts by emphasizing the importance of numerical computing in ML and DL, demonstrating the limitations of standard libraries like NumPy, and introducing the solution offered by JAX. It then guides the reader through the installation of JAX on different computing environments like CPUs, GPUs, and TPUs, and its integration into existing ML and DL projects. The book details the advanced numerical operations and unique features of JAX, including JIT compilation, automatic differentiation, batched operations, and custom gradients. It illustrates how these features can be employed to write code that is both simpler and faster. The book also delves into parallel computation, the effective use of the vmap function, and the use of pmap for distributed computing. Lastly, the reader is walked through the practical application of JAX in training different deep learning models, including RNNs, CNNs, and Bayesian models, with an additional focus on performance-tuning strategies for JAX applications. Key Learnings Mastering the installation and configuration of JAX on various computing environments. Understanding the intricacies of JAX's advanced numerical operations. Harnessing the power of JIT compilation in JAX for accelerated computations. Implementing batched operations using the vmap function for efficient processing. Leveraging automatic differentiation and custom gradients in JAX. Proficiency in using the pmap function for distributed computing in JAX. Training different types of deep learning models using JAX. Applying performance tuning strategies to maximize JAX application efficiency. Integrating JAX into existing machine learning and deep learning projects. Complementing the official JAX documentation with practical, real-world applications. Table of Content Necessity for Google JAX Unravelling JAX Setting up JAX for Machine Learning and Deep Learning JAX for Numerical Computing Diving Deeper into Auto Differentiation and Gradients Efficient Batch Processing with JAX Power of Parallel Computing with JAX Training Neural Networks with JAX Audience This is must read for machine learning and deep learning professionals to be skilled with the most innovative deep learning library. Knowing Python and experience with machine learning is sufficient is desired to begin with this book.
Google Jax Essentials
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Author : Mei Wong
language : en
Publisher: GitforGits
Release Date : 2023-05-31
Google Jax Essentials written by Mei Wong and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Computers categories.
"Google JAX Essentials" is a comprehensive guide designed for machine learning and deep learning professionals aiming to leverage the power and capabilities of Google's JAX library in their projects. Over the course of eight chapters, this book takes the reader from understanding the challenges of deep learning and numerical computations in the existing frameworks to the essentials of Google JAX, its functionalities, and how to leverage it in real-world machine learning and deep learning projects. The book starts by emphasizing the importance of numerical computing in ML and DL, demonstrating the limitations of standard libraries like NumPy, and introducing the solution offered by JAX. It then guides the reader through the installation of JAX on different computing environments like CPUs, GPUs, and TPUs, and its integration into existing ML and DL projects. The book details the advanced numerical operations and unique features of JAX, including JIT compilation, automatic differentiation, batched operations, and custom gradients. It illustrates how these features can be employed to write code that is both simpler and faster. The book also delves into parallel computation, the effective use of the vmap function, and the use of pmap for distributed computing. Lastly, the reader is walked through the practical application of JAX in training different deep learning models, including RNNs, CNNs, and Bayesian models, with an additional focus on performance-tuning strategies for JAX applications. Key Learnings Mastering the installation and configuration of JAX on various computing environments. Understanding the intricacies of JAX's advanced numerical operations. Harnessing the power of JIT compilation in JAX for accelerated computations. Implementing batched operations using the vmap function for efficient processing. Leveraging automatic differentiation and custom gradients in JAX. Proficiency in using the pmap function for distributed computing in JAX. Training different types of deep learning models using JAX. Applying performance tuning strategies to maximize JAX application efficiency. Integrating JAX into existing machine learning and deep learning projects. Complementing the official JAX documentation with practical, real-world applications. Table of Content Necessity for Google JAX Unravelling JAX Setting up JAX for Machine Learning and Deep Learning JAX for Numerical Computing Diving Deeper into Auto Differentiation and Gradients Efficient Batch Processing with JAX Power of Parallel Computing with JAX Training Neural Networks with JAX Audience This is must read for machine learning and deep learning professionals to be skilled with the most innovative deep learning library. Knowing Python and experience with machine learning is sufficient is desired to begin with this book.
Jax Essentials
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-24
Jax Essentials written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-24 with Computers categories.
"JAX Essentials" JAX Essentials is a comprehensive guide designed for engineers, researchers, and practitioners aiming to master the fundamentals and advanced capabilities of Google’s JAX library. Beginning with the foundational principles, the book explores JAX's unique approach to array programming, rooted in functional programming and immutability, and covers its architecture, data structures, and the powerful mechanics of automatic differentiation. The reader learns how JAX integrates with the broader numerical computing landscape—tracing its evolution, relationship with tools like NumPy and XLA, and its relevance in modern machine learning workflows. Building on this foundation, the book delves into advanced topics such as efficient array operations, parallelization strategies, just-in-time compilation, differentiation APIs, and functional control flow constructs. Readers are guided through the intricacies of high-performance and distributed computing, memory optimization, cross-framework interoperability, and scientific computing applications, including linear algebra, spectral analysis, differential equations, and statistical modeling. Clear, practical examples and best practices illustrate how to harness JAX’s capabilities for building custom neural networks, scalable training pipelines, and robust production systems. JAX Essentials further addresses the needs of power users with chapters on extensibility, custom primitives, XLA integration, mixed-precision and distributed training, debugging, and performance tuning. The book concludes with an insightful look at JAX’s ecosystem, emerging libraries, research breakthroughs, and future directions, empowering readers to contribute to and thrive in a vibrant, rapidly evolving open-source community. Whether you are migrating legacy code, scaling scientific workloads, or building state-of-the-art machine learning solutions, this book is your definitive companion for unlocking the potential of JAX.
Deep Learning With Python Third Edition
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Author : Francois Chollet
language : en
Publisher: Simon and Schuster
Release Date : 2025-09-30
Deep Learning With Python Third Edition 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 2025-09-30 with Computers categories.
The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX! Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python. In Deep Learning with Python, Third Edition you’ll discover: • Deep learning from first principles • The latest features of Keras 3 • A primer on JAX, PyTorch, and TensorFlow • Image classification and image segmentation • Time series forecasting • Large Language models • Text classification and machine translation • Text and image generation—build your own GPT and diffusion models! • Scaling and tuning models With over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images. About the book Deep Learning with Python, Third Edition introduces deep learning from scratch. Each chapter introduces practical code examples that build up your understanding of deep learning layer by layer. You’ll appreciate the intuitive explanations, crisp color illustrations, and clear examples. In this expanded third edition you’ll find fresh chapters on the transformers architecture, building your own GPT-like large language model, and image generation with diffusion models. Plus, even DL veterans will benefit from the insightful explanations on the nature of deep learning. About the reader For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the author François Chollet is a software engineer at Google and creator of the Keras deep learning library. Matthew Watson is a core maintainer of the Keras deep learning library, focusing primarily on tools for Natural Language Processing. Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
Jax Essentials
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-24
Jax Essentials written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-24 with Computers categories.
"JAX Essentials" JAX Essentials is a comprehensive guide designed for engineers, researchers, and practitioners aiming to master the fundamentals and advanced capabilities of Google’s JAX library. Beginning with the foundational principles, the book explores JAX's unique approach to array programming, rooted in functional programming and immutability, and covers its architecture, data structures, and the powerful mechanics of automatic differentiation. The reader learns how JAX integrates with the broader numerical computing landscape—tracing its evolution, relationship with tools like NumPy and XLA, and its relevance in modern machine learning workflows. Building on this foundation, the book delves into advanced topics such as efficient array operations, parallelization strategies, just-in-time compilation, differentiation APIs, and functional control flow constructs. Readers are guided through the intricacies of high-performance and distributed computing, memory optimization, cross-framework interoperability, and scientific computing applications, including linear algebra, spectral analysis, differential equations, and statistical modeling. Clear, practical examples and best practices illustrate how to harness JAX’s capabilities for building custom neural networks, scalable training pipelines, and robust production systems. JAX Essentials further addresses the needs of power users with chapters on extensibility, custom primitives, XLA integration, mixed-precision and distributed training, debugging, and performance tuning. The book concludes with an insightful look at JAX’s ecosystem, emerging libraries, research breakthroughs, and future directions, empowering readers to contribute to and thrive in a vibrant, rapidly evolving open-source community. Whether you are migrating legacy code, scaling scientific workloads, or building state-of-the-art machine learning solutions, this book is your definitive companion for unlocking the potential of JAX.
Gpu Accelerated Deep Learning
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Author : Ramchandra S Mangrulkar
language : en
Publisher: Springer Nature
Release Date : 2026-01-01
Gpu Accelerated Deep Learning written by Ramchandra S Mangrulkar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-01 with Computers categories.
Explore the convergence of deep learning and GPU technology. This book is a complete guide for those wishing to use GPUs to accelerate AI workflows. The book is meant to make complex concepts understandable, with step-by-step instructions on how to set up and use GPUs in deep learning applications. Starting with an introduction to the fundamentals, you'll dive into progressive topics like Convolutional Neural Networks (CNNs) and sequence models, exploring how GPU optimization boosts performance. Further, you will learn the power of generative models, and take your skills by deploying AI models on edge devices. Finally, you will master the art of scaling and distributed training to handle large datasets and complex tasks efficiently. This book is your roadmap to becoming proficient in deep learning and harnessing the full potential of GPUs. What You Will Learn: How to apply deep learning techniques on GPUs to solve challenging AI problems. Optimizing neural networks for faster training and inference on GPUs Integration of GPUs with Microsoft Copilots Implementing VAEs (Variational Autoencoders) with TensorFlow and PyTorch Who This Book Is For: Industry IT professionals in AI. Students pursuing undergraduate and postgraduate degrees in Engineering, Computer Science, Data Science.
Mastering Generative Ai Theory Techniques And Real World Applications
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Author : Dr. Mamatha B
language : en
Publisher: NC Publishers
Release Date : 2025-04-15
Mastering Generative Ai Theory Techniques And Real World Applications written by Dr. Mamatha B and has been published by NC Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
Mastering Generative AI is a comprehensive textbook covering the theory, techniques, and real-world applications of generative models like GANs and transformers. It blends foundational concepts with practical coding exercises and ethical insights, making it ideal for students, researchers, and professionals in AI, data science, and related fields.
Google Jax Cookbook
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Author : Zephyr Quent
language : en
Publisher: GitforGits
Release Date : 2024-10-30
Google Jax Cookbook written by Zephyr Quent and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-30 with Computers categories.
This is the practical, solution-oriented book for every data scientists, machine learning engineers, and AI engineers to utilize the most of Google JAX for efficient and advanced machine learning. It covers essential tasks, troubleshooting scenarios, and optimization techniques to address common challenges encountered while working with JAX across machine learning and numerical computing projects. The book starts with the move from NumPy to JAX. It introduces the best ways to speed up computations, handle data types, generate random numbers, and perform in-place operations. It then shows you how to use profiling techniques to monitor computation time and device memory, helping you to optimize training and performance. The debugging section provides clear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors. The book goes on to show you how to master Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations. If you want to optimize training processes, this book has you covered. It includes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai. You'll learn how to visualize model performance and monitor metrics to assess training progress effectively. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly. Key Learnings Get your calculations done faster by moving from NumPy to JAX's optimized framework. Make your training pipelines more efficient by profiling how long things take and how much memory they use. Use debugging techniques to fix runtime issues like shape mismatches and numerical instability. Get to grips with Pytrees for managing complex, nested data structures across various machine learning tasks. Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost. Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy. Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics. Use advanced visualization techniques, like confusion matrices and learning curves, to make model evaluation more effective. Create your own neural networks and optimizers directly in JAX so you have full control of the architecture. Use serialization techniques to save, load, and transfer models and training checkpoints efficiently. Table of Content Transition NumPy to JAX Profiling Computation and Device Memory Debugging Runtime Values and Errors Mastering Pytrees for Data Structures Exporting and Serialization Type Promotion Semantics and Mixed Precision Integrating Foreign Functions (FFI) Training Neural Networks with JAX
Ajax Programming For The Absolute Beginner
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Author : Jerry Lee Ford
language : en
Publisher: Muska/Lipman
Release Date : 2009
Ajax Programming For The Absolute Beginner written by Jerry Lee Ford and has been published by Muska/Lipman this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
Learn Ajax the easy way! If you are new to programming with Ajax and are looking for a solid introduction, this is the book for you. Developed by computer science instructors, books in the "for the absolute beginner" series teach the principles of programming through simple game creation. You will acquire the skills that you need for more practical programming applications and will learn how these skills can be put to use in real-world scenarios. Best of all, by the time you finish the book you will be able to apply the basic principles you've learned to the next programming language you tackle.
B To B
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Author :
language : en
Publisher:
Release Date : 2002
B To B written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Advertising categories.