Jaxcore


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Deep Learning with JAX


Deep Learning with JAX

Author: Grigory Sapunov

language: en

Publisher: Simon and Schuster

Release Date: 2024-10-29


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"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" --Publisher's description.

An Analysis of HUD/FHA Single Family Mortgage Activity in the Southeast


An Analysis of HUD/FHA Single Family Mortgage Activity in the Southeast

Author: United States. Department of Housing and Urban Development. Region IV. Office of Program Planning and Evaluation

language: en

Publisher:

Release Date: 1978


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Annual Report


Annual Report

Author: Kentucky. Dept. of Revenue

language: en

Publisher:

Release Date: 1917


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