Deep Learning with JAX

Grigory Sapunov

ISBN 10: 1633438880 / ISBN 13: 9781633438880

Verlag: Manning Publications, US, 2024

Beschreibung

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 calculationsBuild differentiable models with JAX primitivesRun distributed and parallelized computations with JAXUse high-level neural network libraries such as Flax and HaikuLeverage 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.

Bestandsnummer des Verkäufers LU-9781633438880

Bibliografische Details

• Titel: Deep Learning with JAX
• Verlag: Manning Publications, US
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 1633438880
• ISBN-13: 9781633438880
• Einband: Hardback
• Zustand: New

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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.