Deep Learning in JAX with Haiku (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"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 insig...
"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.
"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.
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