Название: Deep Learning with JAX (MEAP v6)
Автор: Grigory Sapunov
Издательство: Manning Publications
Год: 2023
Страниц: 211
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

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 calculations
Build differentiable models with JAX primitives
Run distributed and parallelized computations with JAX
Use high-level neural network libraries such as Flax and Haiku
Leverage libraries and modules from the JAX ecosystem

JAX is a Python mathematics library with a NumPy interface developed by Google. It is heavily used for machine learning research, and it seems that JAX has already become the #3 Deep Learning framework (after TensorFlow and PyTorch). It also became the main Deep Learning framework in companies such as DeepMind, and more and more of Google’s own research use JAX. JAX promotes a functional programming paradigm in Deep Learning. It has powerful function
transformations such as taking gradients of a function, JIT-compilation with XLA, auto-vectorization, and parallelization. JAX supports GPU and TPU and provides great performance.

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.

about the technology
The 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.

about the book
Deep Learning with JAX teaches 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!