Deep Learning with JAX by Grigory Sapunov

Deep Learning with JAX by Grigory Sapunov

Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. In Deep Learning with JAX you will learn how to: Use JAX for numerical calculations Build differentiable models with JAX primitives Run distributed and parallelized computations with JAX Use high-level neural network libraries such as Flax and Haiku Leverage libraries and modules from the JAX ecosystem 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.

Grigory Sapunov is a co-founder and CTO of Intento. He is a software engineer with more than twenty years of experience. Grigory holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning.

SKU	Non disponible
ISBN 13	9781633438880
ISBN 10	1633438880
Titre	Deep Learning with JAX
Auteur	Grigory Sapunov
État	Non disponible
Éditeur	Manning Publications
Année de publication	2024-10-29
Nombre de pages	408
Note de couverture	La photo du livre est présentée à titre d'illustration uniquement. La reliure, la couverture ou l'édition réelle peuvent varier.
Note	Non disponible