{"product_id":"google-jax-cookbook-perform-machine-learning-and-numerical-computing-with-combined-capabilities-of-tensorflow-and-numpy-9788197950414","title":"Google JAX Cookbook: Perform machine learning and numerical computing with combined capabilities of TensorFlow and NumPy","description":"\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eThe book starts with the \u003cstrong\u003emove from NumPy to JAX\u003c\/strong\u003e. It introduces the \u003cstrong\u003ebest ways to speed up computations, handle data types, generate random numbers, and perform in-place operations\u003c\/strong\u003e. It then shows you how to \u003cstrong\u003euse profiling techniques to monitor computation time and device memory, helping you to optimize training and performance\u003c\/strong\u003e. The debugging section provides \u003cstrong\u003eclear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors\u003c\/strong\u003e. The book goes on to show you how to \u003cstrong\u003emaster Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003eIf you want to optimize training processes, this book has you covered. It \u003cstrong\u003eincludes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai\u003c\/strong\u003e. You'll learn how to \u003cstrong\u003evisualize model performance and monitor metrics to assess training progress effectively\u003c\/strong\u003e. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eKey Learnings\u003cp\u003eGet your calculations done faster by moving from NumPy to JAX's optimized framework.\u003c\/p\u003e\u003cp\u003eMake your training pipelines more efficient by profiling how long things take and how much memory they use.\u003c\/p\u003e\u003cp\u003eUse debugging techniques to fix runtime issues like shape mismatches and numerical instability.\u003c\/p\u003e\u003cp\u003eGet to grips with Pytrees for managing complex, nested data structures across various machine learning tasks.\u003c\/p\u003e\u003cp\u003eUse JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost.\u003c\/p\u003e\u003cp\u003eTake advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy.\u003c\/p\u003e\u003cp\u003eKeep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics.\u003c\/p\u003e\u003cp\u003eCreate your own neural networks and optimizers directly in JAX so you have full control of the architecture.\u003c\/p\u003e\u003cp\u003eUse serialization techniques to save, load, and transfer models and training checkpoints efficiently.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eTable of Content\u003cp\u003eTransition NumPy to JAX\u003c\/p\u003e\u003cp\u003eProfiling Computation and Device Memory\u003c\/p\u003e\u003cp\u003eDebugging Runtime Values and Errors\u003c\/p\u003e\u003cp\u003eMastering Pytrees for Data Structures\u003c\/p\u003e\u003cp\u003eExporting and Serialization\u003c\/p\u003e\u003cp\u003eType Promotion Semantics and Mixed Precision\u003c\/p\u003e\u003cp\u003eIntegrating Foreign Functions (FFI)\u003c\/p\u003e\u003cp\u003eTraining Neural Networks with JAX\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Gitforgits\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 10\/30\/2024\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9788197950414\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 252\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.97lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.25h x 7.50w x 0.53d","brand":"Zephyr Quent","offers":[{"title":"Default Title","offer_id":51280778363061,"sku":"9788197950414","price":59.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0473\/0804\/6492\/files\/img_d7e5e0a2-6ebf-4f13-9488-c365d19a64ae.jpg?v=1751468964","url":"https:\/\/pastforward.org\/products\/google-jax-cookbook-perform-machine-learning-and-numerical-computing-with-combined-capabilities-of-tensorflow-and-numpy-9788197950414","provider":"Past Forward","version":"1.0","type":"link"}