Introduction to Google JAX: Your Guide to Understanding JAX

Google's JAX library is a powerful tool for high-performance machine learning research, leveraging hardware accelerators like GPUs and TPUs while maintaining Python's simplicity and expressiveness. It offers rapid application execution, improved scalability, and simplified debugging and optimization. JAX's foundational concepts include array programming and function transformations, which allow for the automatic differentiation, vectorization, parallelization, and compilation to accelerators. It is an extension of the widely used NumPy toolkit for array programming, allowing for scientific computing, numerical analysis, and machine learning.

This book covers various aspects of machine learning, including linear algebra, optimization, deep learning, neural networks, Bayesian inference, probabilistic programming, and more. The course will teach users how to use JAX and its built-in libraries and tools, including PAX and FLAX, to construct scalable neural network models with high-level abstractions. The course also provides the opportunity to gain experience with these packages, making it easier to build models of scalable neural networks.

The material in this book is accessible and easy to understand for those new to Python and machine learning, but it would be beneficial to have some experience with NumPy and array programming. The book is structured so that each chapter presents a new idea or subject, explains it with examples and activities, and then gives reading recommendations. This book will walk you through the ins and outs of JAX, so you can start using it in your own machine learning applications with confidence.