Accelerate Deep Learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of Deep Learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. Deep Learning with JAX is a hands-on guide to using JAX for Deep Learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. The JAX Python mathematics library is used by many successful Deep Learning organizations, including Google’s groundbreaking DeepMind team. This exciting newcomer already boasts an amazing ecosystem of tools including high-level Deep Learning libraries Flax by Google, Haiku by DeepMind, gradient processing and optimization libraries, libraries for evolutionary computations, Federated Learning, and much more! JAX brings a functional programming mindset to Python Deep Learning, letting you improve your composability and parallelization in a cluster. For intermediate Python programmers who are familiar with Deep Learning.