Deep Learning With Jax

DOWNLOAD

Download Deep Learning With Jax PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Deep Learning With Jax book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Deep Learning With Jax

DOWNLOAD
Author : Grigory Sapunov
language : en
Publisher: Simon and Schuster
Release Date : 2024-10-29

Deep Learning With Jax written by Grigory Sapunov 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 2024-10-29 with Computers categories.

"The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google's Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. Deep learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX's concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You'll learn how to use JAX's ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment" --Publisher's description.

Deep Learning In Jax With Haiku

DOWNLOAD
Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-10-25

Deep Learning In Jax With Haiku 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 2025-10-25 with Computers categories.

"Deep Learning in JAX with Haiku" "Deep Learning in JAX with Haiku" is an authoritative and comprehensive guide to next-generation deep learning, leveraging the strengths of the JAX ecosystem and its cutting-edge Haiku library. Designed for both researchers and practitioners, this book masterfully unpacks the foundational principles of JAX, from its powerful autograd and just-in-time compilation features to its functional programming paradigms and advanced array transformations. Readers are equipped to navigate the comparative landscape of modern machine learning frameworks, with focused insights into the scenarios where JAX and Haiku stand apart for scalability, efficiency, and clarity. Building from first principles, the book delves deeply into the structure, design, and modular construction of neural networks using Haiku. Through practical chapters, it covers a wide suite of architectures—including multi-layer perceptrons, deep convolutional and recurrent networks, transformers, and advanced parameter sharing techniques—while providing guidance on training, optimization, and model state management at research and industrial scale. In addition, comprehensive sections illuminate advanced topics such as generative modeling, self-supervised learning, graph neural networks, meta-learning, and reinforcement learning, empowering readers to extend and innovate upon the latest research. With a strong emphasis on rigorous experimentation, responsible AI, and robust deployment, "Deep Learning in JAX with Haiku" explores evaluation, explainability, adversarial robustness, and operational best practices spanning from checkpointing and distributed training to CI/CD, compliance, and model health in production. The concluding chapters cast a forward-looking vision, exploring emerging trends in architecture, hardware acceleration, federated learning, AutoML, and open science. This is an indispensable resource for anyone seeking to master the art and science of deep learning with JAX and Haiku—offering both foundational knowledge and explorations of the frontier.

Flax Deep Learning With Jax

DOWNLOAD
Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-08-20

Flax Deep Learning With Jax 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 2025-08-20 with Computers categories.

"Flax Deep Learning with JAX" "Flax Deep Learning with JAX" is the definitive guide for researchers, practitioners, and engineers seeking to harness the full capability of the JAX ecosystem for deep learning. Beginning with a thorough exploration of JAX’s foundational concepts—functional programming, automatic differentiation, JIT compilation, and parallel computing—the book establishes a strong base for readers to appreciate both the power and the nuance of high-performance, scalable numerical computing. Integral aspects such as random number management, numerical precision, and sophisticated program transformations are clearly articulated to lay the groundwork for building complex, production-grade deep learning systems. The core of the book delves into the Flax framework, emphasizing its modular, functional design and hierarchical model-building paradigm. Readers learn to construct a diverse array of modern neural network architectures—including MLPs, CNNs, RNNs, transformers, and graph neural networks—while mastering the intricacies of model state, custom layers, and parameter management. Step-by-step coverage of training workflows spans data pipelines, optimization, gradient manipulation, and robust experiment tracking, equipping practitioners to train and scale models efficiently from initial data loading to advanced distributed and multi-device training strategies. Recognizing the realities and responsibilities of deploying deep learning models, this comprehensive guide addresses model evaluation, export, production deployment, edge inference, and best practices for model monitoring and CI/CD integration. The final chapters provide critical insight into security, ethical AI, and reproducibility, alongside emerging research directions and community-driven advancements. "Flax Deep Learning with JAX" is an essential resource for anyone aspiring to build reliable, responsible, and state-of-the-art machine learning systems with JAX and Flax.

Hands On Deep Learning With Jax Flax And Pytorch

DOWNLOAD
Author : BENJAMIN. NEUDORF
language : en
Publisher: Independently Published
Release Date : 2025-09-04

Hands On Deep Learning With Jax Flax And Pytorch written by BENJAMIN. NEUDORF and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-04 with Computers categories.

Hands-On Deep Learning with JAX, Flax, and PyTorch: A Practical Guide for Beginners and Beyond Are you fascinated by the world of artificial intelligence but feel overwhelmed by technical jargon and complex theory? Do you wish someone would break down deep learning step by step, guiding you with warmth, encouragement, and hands-on examples? You're not alone-and you're exactly who this book was written for. Hands-On Deep Learning with JAX, Flax, and PyTorch is your friendly mentor in book form, designed to empower absolute beginners and aspiring programmers to confidently explore modern AI from the ground up. No prior experience? No problem! Every chapter is crafted with care to help you overcome self-doubt, celebrate progress, and master essential skills in a welcoming, approachable way. What makes this book different? Step-by-Step Explanations: Learn by doing, not just by reading. Each concept is introduced with simple language, practical code, and real-world projects using JAX, Flax, and PyTorch. Beginner-Friendly: Written for curious minds with zero technical background. Every topic is broken down into bite-sized, manageable steps. Project-Based Learning: Build hands-on projects-like image classifiers and natural language processing tools-while gaining real experience with the world's most exciting AI frameworks. Mistakes are Celebrated: The path to mastery is full of experiments and surprises. Here, mistakes are welcomed as part of your journey, and every small win is celebrated. Practical, Modern, and Future-Proof: Get up to speed with the latest deep learning techniques, workflows, and tools used by researchers and industry professionals. By the end of this book, you'll be able to: Understand the core principles of deep learning and neural networks Work confidently with JAX, Flax, and PyTorch in Python Build, train, and evaluate your own machine learning models from scratch Apply deep learning to real-world tasks, from image recognition to natural language processing Develop the confidence to experiment, troubleshoot, and grow as an AI practitioner Perfect for: Beginners, students, career changers, self-learners, and anyone who wants a supportive, hands-on introduction to deep learning. Your journey starts here. If you've ever felt intimidated by technology or unsure where to begin, this book will be your supportive companion-cheering you on, answering your questions, and guiding you one step at a time. With every project and every line of code, you'll discover just how much you're capable of achieving. Ready to unlock the power of deep learning? Turn the page and start building your future-one hands-on project at a time!

Machine Learning For Jax

DOWNLOAD
Author : GILBERTO. NEAL
language : en
Publisher: Independently Published
Release Date : 2025-02-27

Machine Learning For Jax written by GILBERTO. NEAL and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-27 with Computers categories.

Machine learning is evolving rapidly, and efficiency is more critical than ever. Machine Learning for JAX is your ultimate guide to leveraging JAX for high-performance deep learning, large-scale AI training, and cutting-edge research. Whether you're a researcher, engineer, or AI enthusiast, this book will equip you with the tools to build faster, scalable, and optimized models using JAX's powerful automatic differentiation, JIT compilation, and GPU/TPU acceleration. This book provides comprehensive and hands-on coverage of JAX, from the fundamentals of numerical computing to advanced AI applications, including reinforcement learning, large language models (LLMs), and distributed training. You'll explore real-world industry use cases, optimize AI workflows with pmap and pjit, and learn how to handle massive datasets efficiently. Through detailed explanations, real-world examples, and working code implementations, you'll gain a deep practical understanding of JAX and its role in accelerating machine learning. Each chapter breaks down complex topics in an easy-to-follow manner, ensuring that both beginners and experienced developers can harness the full potential of JAX. What You Will Learn: Fundamentals of JAX and how it differs from NumPy and TensorFlow JIT compilation and vectorization for massive speedups Optimization techniques using SGD, Adam, and RMSprop in JAX Distributed training with multi-GPU and TPU acceleration Building and optimizing large-scale AI models like VAEs, GANs, and LLMs Using JAX in scientific computing and graph neural networks (GNNs) Real-world production use cases and how JAX integrates with Google's AI ecosystem Why This Book? Unlike other deep learning books, Machine Learning for JAX goes beyond the basics and focuses on practical, real-world applications. You won't just learn theory-you'll build, optimize, and scale AI models like a pro. Whether you're working on academic research, AI startups, or enterprise-scale ML systems, this book will elevate your machine learning capabilities. JAX is redefining the future of machine learning and AI research. Don't get left behind. Whether you're an ML researcher, software engineer, or data scientist, this book will empower you with the knowledge and skills to stay ahead in the AI revolution. Get your copy now and unlock the full power of JAX!

Google Jax Essentials

DOWNLOAD
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.

Deep Learning In Python Using Jax

DOWNLOAD
Author : Newman Chandler
language : en
Publisher: Independently Published
Release Date : 2025-07-28

Deep Learning In Python Using Jax written by Newman Chandler and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-28 with Computers categories.

Deep Learning in Python Using JAX: Achieve Breakthrough Speed and Robust Scalability for AI Applications Struggling to scale your deep learning projects beyond a single GPU? Wondering how to squeeze every ounce of performance from your Python code? Deep Learning in Python Using JAX offers a practical blueprint for building and scaling modern neural networks with unmatched speed and efficiency. What this book delivers: Harness the power of JAX's automatic differentiation and XLA compiler to transform your deep learning workflows. You'll learn how to: Accelerate model development with grad, jit, vmap, and pmap Implement custom layers, loss functions, and optimizers from first principles Scale seamlessly across GPUs and TPUs for high-throughput training Leverage high-level libraries (Flax, Optax) and alternatives (Haiku, Equinox) Optimize performance using mixed precision, profiler tools, and XLA flags Deploy production-ready models via ONNX, TensorFlow Serving, and REST APIs Each chapter combines clear explanations with hands-on examples-no vague theory, just complete, ready-to-run code. You'll gain confidence in writing pure-function neural networks, mastering core JAX transformations, and integrating these techniques into real-world AI applications. Is this book right for you? You're an ML engineer or researcher tired of slow Python loops and rigid frameworks. You value clean, functional code that compiles to lightning-fast kernels. You need a roadmap for both low-level customization and high-level productivity. Take the next step toward high-performance, scalable AI: add Deep Learning in Python Using JAX to your library today and propel your projects to new levels of speed and robustness.

The Llm Engineer S Playbook Mastering The Development Of Large Language Models For Real World Applications

DOWNLOAD
Author : Leona Lang
language : en
Publisher: DIGITAL BLUE INC.
Release Date : 2025-03-31

The Llm Engineer S Playbook Mastering The Development Of Large Language Models For Real World Applications written by Leona Lang and has been published by DIGITAL BLUE INC. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-31 with Computers categories.

The world of artificial intelligence is rapidly evolving, and at the heart of this revolution are Large Language Models (LLMs). These powerful tools are transforming how we interact with technology, offering unprecedented capabilities in natural language processing. The LLM Engineer's Playbook is an essential guide for anyone looking to navigate the complexities of developing and deploying LLMs in practical, real-world scenarios. This book provides a comprehensive roadmap for engineers, developers, and tech enthusiasts eager to harness the potential of LLMs, offering a blend of theoretical insights and hands-on techniques. Within these pages, you'll find a rich array of content designed to elevate your understanding and skills in LLM development. The book covers foundational concepts, ensuring even those new to the field can follow along, and progressively delves into more advanced topics. Key sections include the architecture and functioning of LLMs, data preparation and preprocessing, model training and fine-tuning, and best practices for deployment and maintenance. Each chapter is crafted to build on the previous one, creating a seamless learning experience. The practical examples and case studies illustrate how LLMs can be applied in various industries, from enhancing customer service chatbots to revolutionizing content creation and beyond.

Deep Learning With Python Third Edition

DOWNLOAD
Author : Francois Chollet
language : en
Publisher: Simon and Schuster
Release Date : 2025-10-21

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 2025-10-21 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 technology In less than a decade, deep learning has changed the world—twice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider. About the book Deep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer. What's inside • Hands-on, code-first learning • Comprehensive, from basics to generative AI • Intuitive and easy math explanations • Examples in Keras, PyTorch, JAX, and TensorFlow About the reader For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required. About the author François Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras. Table of Contents 1 What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to TensorFlow, PyTorch, JAX, and Keras 4 Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 A deep dive on Keras 8 Image classification 9 ConvNet architecture patterns 10 Interpreting what ConvNets learn 11 Image segmentation 12 Object detection 13 Timeseries forecasting 14 Text classification 15 Language models and the Transformer 16 Text generation 17 Image generation 18 Best practices for the real world 19 The future of AI 20 Conclusions

Practical Deep Learning In Python

DOWNLOAD
Author : Marcus C Lauritsen
language : en
Publisher: Independently Published
Release Date : 2025-08

Practical Deep Learning In Python written by Marcus C Lauritsen and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08 with Computers categories.

Unlock the Power of Deep Learning-No Experience Needed Are you fascinated by artificial intelligence but overwhelmed by where to begin? Do the endless tutorials, frameworks, and jargon make deep learning seem out of reach? This book is your roadmap-whether you're a complete beginner, a student, or a developer eager to build real AI solutions with confidence. Practical Deep Learning in Python gently guides you from your very first neural network to advanced projects, all with hands-on, step-by-step instructions. There's no need for a PhD or prior experience-just curiosity and the desire to learn. Every concept is broken down with plain language, practical tips, and complete code examples you can run, modify, and make your own. What Makes This Book Different? Four Frameworks, One Journey: Master PyTorch, TensorFlow, Keras, and JAX-discover each tool's strengths, see how they compare, and develop the flexibility to tackle any project. Project-Based Learning: Build image classifiers, sentiment analysis models, time series predictors, and more-across real-world datasets and domains. Step-by-Step Guidance: Each chapter builds on the last, ensuring you gain both a solid foundation and advanced techniques, including transfer learning, model optimization, and deployment. Beginner Friendly, Expert-Ready: Start from scratch and grow at your own pace. All essential Python tools and setup steps are covered, with troubleshooting tips to keep you moving forward. Encouraging and Supportive: Mistakes are normal-progress is celebrated at every stage. You'll learn how to experiment, debug, and grow, turning setbacks into breakthroughs. You'll Gain: The confidence to build, train, and evaluate deep learning models from the ground up Practical skills with today's most important Python AI frameworks A clear understanding of core deep learning concepts, from neural networks to deployment A flexible mindset for adapting to new tools and challenges as the AI field evolves Key Takeaways: Hands-on code in every chapter-experiment, modify, and make it your own Real-world projects: image classification, NLP, time series, and more Side-by-side framework comparisons for deep learning mastery Guidance on environment setup, hardware acceleration, and troubleshooting Insider tips for best practices, reproducibility, and staying up-to-date in AI Ready to Build Something Amazing? Start your practical journey into deep learning today-turn your curiosity into real skills, and your skills into intelligent solutions that make a difference. With this book as your mentor, you'll discover that anyone can master deep learning-one step at a time.