Machine Learning for JAX: Building Scalable, Fast, and Flexible AI Models for the Future of Computing - Brossura
Sinossi
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
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!
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Paperback. Condizione: new. Paperback. 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 TensorFlowJIT compilation and vectorization for massive speedupsOptimization techniques using SGD, Adam, and RMSprop in JAXDistributed training with multi-GPU and TPU accelerationBuilding and optimizing large-scale AI models like VAEs, GANs, and LLMsUsing JAX in scientific computing and graph neural networks (GNNs)Real-world production use cases and how JAX integrates with Google's AI ecosystemWhy 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! This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798312408386
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Paperback. Condizione: new. Paperback. 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 TensorFlowJIT compilation and vectorization for massive speedupsOptimization techniques using SGD, Adam, and RMSprop in JAXDistributed training with multi-GPU and TPU accelerationBuilding and optimizing large-scale AI models like VAEs, GANs, and LLMsUsing JAX in scientific computing and graph neural networks (GNNs)Real-world production use cases and how JAX integrates with Google's AI ecosystemWhy 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! This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798312408386
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