Deep learning with JAX

• Verfasst von: Sapunov, Grigory [VerfasserIn]
• Titel: Deep learning with JAX
• Verf.angabe: Grigory Sapunov
• Verlagsort: [United States]
• Verlag: Manning
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: 1 online resource
• Fussnoten: Vendor-supplied metadata
• ISBN: 978-1-63343-888-0
• ISBN: 1-63343-888-0
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Apprentissage profond
• K10plus-PPN: 191049674X
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4629541284
• Lokale URL Inst.: Zum Volltext

Abstract

Accelerate deep learning and other number-intensive tasks with JAX, Google⁰́₉s 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 Google⁰́₉s 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. 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 Leverage libraries and modules from the JAX ecosystem 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 JAX⁰́₉s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You⁰́₉ll learn how to use JAX⁰́₉s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment.