Deep Learning With Jax, Grigory Sapunov

Deep Learning With Jax, Grigory Sapunov купить книгу в Україні \nОбкладинка - м"яка \n \nРік видання - 2024 \n \nКількість сторінок - 375 \n \nПапір - білий, офсет \nПро книгу Deep Learning With Jax, Grigory Sapunov \nAccelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. \n \nIn \n \nDeep Learning with JAX \n \nyou will learn how to: \n \nUse JAX for numerical calculations \n \nBuild differentiable models with JAX primitives \n \nRun distributed and parallelized computations with JAX \n \nUse high-level neural network libraries such as Flax and Haiku \n \nLeverage libraries and modules from the JAX ecosystem \n \nThe 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. \n \nDeep Learning with JAX \n \nis 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 \nAbout the technology \n \nThe JAX Python mathematics library is used by many successful deep learning organizations, including Google’s groundbreaking DeepMind team. This exciting newcomer already boasts an amazing ecosystem of tools including high-level deep learning libraries Flax by Google, Haiku by DeepMind, gradient processing and optimization libraries, libraries for evolutionary computations, federated learning, and much more! JAX brings a functional programming mindset to Python deep learning, letting you improve your composability and parallelization in a cluster. \n \nAbout the book \n \nDeep Learning with JAX \n \nteaches 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! \n \nAbout the reader \n \nFor intermediate Python programmers who are familiar with deep learning. \n \nAbout the author \n \nGrigory Sapunov \n \nis 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. \n \nRead more \nDeep Learning With Jax, Grigory Sapunov

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