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In den WarenkorbHardback. Zustand: 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.
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In den WarenkorbHardback. Zustand: 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.
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In den WarenkorbHardback. Zustand: 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.
Verlag: Manning Publications 10/29/2024, 2024
ISBN 10: 1633438880 ISBN 13: 9781633438880
Sprache: Englisch
Anbieter: BargainBookStores, Grand Rapids, MI, USA
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In den WarenkorbHardback. Zustand: 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.
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Verlag: Manning Publications Okt 2024, 2024
ISBN 10: 1633438880 ISBN 13: 9781633438880
Sprache: Englisch
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Verlag: Manning Publications, New York, 2024
ISBN 10: 1633438880 ISBN 13: 9781633438880
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In den WarenkorbHardcover. Zustand: new. Hardcover. Accelerate deep learning and other number-intensive tasks with JAX, Googles 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 Googles 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 JAXs concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. Youll learn how to use JAXs ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. Accelerate deep learning and other number-intensive tasks with JAX, Googles awesome high-performance numerical computing library. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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