{"product_id":"9781633438880","title":"Deep Learning with JAX","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eSapunov, Grigory\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10\/29\/2024\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eAccelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThe 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.\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eDeep Learning with JAX\u003c\/i\u003e you will learn how to:\u003cul\u003e\n\u003cli\u003eUse JAX for numerical calculations\u003c\/li\u003e\n\u003cli\u003eBuild differentiable models with JAX primitives\u003c\/li\u003e\n\u003cli\u003eRun distributed and parallelized computations with JAX\u003c\/li\u003e\n\u003cli\u003eUse high-level neural network libraries such as Flax\u003c\/li\u003e\n\u003cli\u003eLeverage libraries and modules from the JAX ecosystem\u003c\/li\u003e\n\u003c\/ul\u003e\u003ci\u003eDeep Learning with JAX\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eGoogle’s JAX offers a fresh vision for deep learning. This powerful library gives you fine control over low level processes like gradient calculations, delivering fast and efficient model training and inference, especially on large datasets. JAX has transformed how research scientists approach deep learning. Now boasting a robust ecosystem of tools and libraries, JAX makes evolutionary computations, federated learning, and other performance-sensitive tasks approachable for all types of applications.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eDeep Learning with JAX\u003c\/i\u003e teaches you to build effective neural networks with JAX. In this example-rich book, you’ll discover how JAX’s unique features help you tackle important deep learning performance challenges, like distributing computations across a cluster of TPUs. You’ll put the library into action as you create an image classification tool, an image filter application, and other realistic projects. The nicely-annotated code listings demonstrate how JAX’s functional programming mindset improves composability and parallelization.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eUse JAX for numerical calculations\u003c\/li\u003e\n\u003cli\u003eBuild differentiable models with JAX primitives\u003c\/li\u003e\n\u003cli\u003eRun distributed and parallelized computations with JAX\u003c\/li\u003e\n\u003cli\u003eUse high-level neural network libraries such as Flax\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eFor intermediate Python programmers who are familiar with deep learning.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eGrigory Sapunov\u003c\/b\u003e holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning.\u003cbr\u003e\u003cbr\u003eThe technical editor on this book was \u003cb\u003eNicholas McGreivy\u003c\/b\u003e.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart 1\u003cbr\u003e1 When and why to use JAX\u003cbr\u003e2 Your first program in JAX\u003cbr\u003ePart 2\u003cbr\u003e3 Working with arrays\u003cbr\u003e4 Calculating gradients\u003cbr\u003e5 Compiling your code\u003cbr\u003e6 Vectorizing your code\u003cbr\u003e7 Parallelizing your computations\u003cbr\u003e8 Using tensor sharding\u003cbr\u003e9 Random numbers in JAX\u003cbr\u003e10 Working with pytrees\u003cbr\u003ePart 3\u003cbr\u003e11 Higher-level neural network libraries\u003cbr\u003e12 Other members of the JAX ecosystem\u003cbr\u003eA Installing JAX\u003cbr\u003eB Using Google Colab\u003cbr\u003eC Using Google Cloud TPUs\u003cbr\u003eD Experimental parallelization","brand":"Manning","offers":[{"title":"Default Title","offer_id":46067823018239,"sku":"9781633438880","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_6cfcf312-484b-4357-a113-e564bd5dfea6.jpg?v=1771351524","url":"https:\/\/massivebookshop.com\/products\/9781633438880","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}