{"product_id":"google-jax-essentials-a-quick-practical-learning-of-blazing-fast-library-for-machine-learning-and-deep-learning-projects-paperback","title":"Google JAX Essentials: A quick practical learning of blazing-fast library for machine learning and deep learning projects - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eMei Wong\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\"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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003eKey Learnings\u003cul\u003e\n\u003cli\u003eMastering the installation and configuration of JAX on various computing environments.\u003c\/li\u003e\n\u003cli\u003eUnderstanding the intricacies of JAX's advanced numerical operations.\u003c\/li\u003e\n\u003cli\u003eHarnessing the power of JIT compilation in JAX for accelerated computations.\u003c\/li\u003e\n\u003cli\u003eImplementing batched operations using the vmap function for efficient processing.\u003c\/li\u003e\n\u003cli\u003eLeveraging automatic differentiation and custom gradients in JAX.\u003c\/li\u003e\n\u003cli\u003eProficiency in using the pmap function for distributed computing in JAX.\u003c\/li\u003e\n\u003cli\u003eTraining different types of deep learning models using JAX.\u003c\/li\u003e\n\u003cli\u003eApplying performance tuning strategies to maximize JAX application efficiency.\u003c\/li\u003e\n\u003cli\u003eIntegrating JAX into existing machine learning and deep learning projects.\u003c\/li\u003e\n\u003cli\u003eComplementing the official JAX documentation with practical, real-world applications.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eTable of Content\u003col\u003e\n\u003cli\u003eNecessity for Google JAX\u003c\/li\u003e\n\u003cli\u003eUnravelling JAX\u003c\/li\u003e\n\u003cli\u003eSetting up JAX for Machine Learning and Deep Learning\u003c\/li\u003e\n\u003cli\u003eJAX for Numerical Computing\u003c\/li\u003e\n\u003cli\u003eDiving Deeper into Auto Differentiation and Gradients\u003c\/li\u003e\n\u003cli\u003eEfficient Batch Processing with JAX\u003c\/li\u003e\n\u003cli\u003ePower of Parallel Computing with JAX\u003c\/li\u003e\n\u003cli\u003eTraining Neural Networks with JAX\u003c\/li\u003e\n\u003c\/ol\u003e\u003cbr\u003eAudience\u003cp\u003eThis 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\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 120\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.25 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 31, 2023\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":52494104330547,"sku":"9788196288358","price":71.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0300\/5595\/6612\/files\/wLAU7yrjCU9788196288358.webp?v=1759971286","url":"https:\/\/www.vysn.com\/en-ca\/products\/google-jax-essentials-a-quick-practical-learning-of-blazing-fast-library-for-machine-learning-and-deep-learning-projects-paperback","provider":"VYSN","version":"1.0","type":"link"}