{"id":13025,"date":"2026-08-10T10:53:37","date_gmt":"2026-08-10T10:53:37","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-deep-learning-with-jax-video-edition-2024-11\/"},"modified":"2026-08-10T10:53:37","modified_gmt":"2026-08-10T10:53:37","slug":"oreilly-deep-learning-with-jax-video-edition-2024-11","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-deep-learning-with-jax-video-edition-2024-11\/","title":{"rendered":"Oreilly \u2013 Deep Learning with JAX, Video Edition 2024-11"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Deep Learning with JAX, Video Edition. This course teaches participants how to accelerate high-speed numerical computation and deep learning using Google\u2019s JAX library. The JAX library addresses fundamental performance challenges at the heart of deep learning and scientific computing by combining Google\u2019s accelerated linear algebra platform with an optimized version of NumPy, delivering dramatic increases in performance and low-level transformations. As a hands-on guide, this course builds on the understanding of JAX concepts through concrete examples by Google expert Grigory Saponov. JAX makes model training and inference fast and efficient, especially on large datasets, by providing fine-grained control over processes such as gradient computation. With a robust ecosystem, the library enables evolutionary computing, federated learning, and other performance-sensitive tasks. During this course, participants will learn how unique JAX features such as functional programming help solve performance challenges such as distributing computations across a cluster of tensor processing units by building an image classifier, an image filter application, and other practical projects. They will also learn how to leverage the JAX ecosystem, including high-level libraries, as well as its integration with TensorFlow and PyTorch for data loading and deployment.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n