Articles liés à Deep Learning With Jax
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
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.
À propos de la quatrième de couverture
From the back cover:
Deep Learning with JAX teaches you how to use JAX and its ecosystem to build neural networks. You’ll learn by exploring interesting examples including an image classification tool, an image filter application, and a massive scale neural network with distributed training across a cluster of TPUs. Discover how to work with JAX for hardware and other low-level aspects and how to solve common machine learning problems with JAX. By the time you’re finished with this awesome book, you’ll be ready to start applying JAX to your own research and prototyping!
About the reader:
For intermediate Python programmers who are familiar with deep learning.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Deep Learning with JAX
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Hardback. Etat : New. 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. N° de réf. du vendeur LU-9781633438880
Quantité disponible : 1 disponible(s)
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Deep Learning With JAX
Edité par
Manning, 2024
ISBN 10 : 1633438880
ISBN 13 : 9781633438880
Ancien ou d'occasion
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Deep Learning with JAX
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Manning Publications, US, 2024
ISBN 10 : 1633438880
ISBN 13 : 9781633438880
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Couverture rigide
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Évaluation du vendeur 5 sur 5 étoiles
Hardback. Etat : New. 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. N° de réf. du vendeur LU-9781633438880
Quantité disponible : 10 disponible(s)