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
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.
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!
about the reader
For intermediate Python programmers who are familiar with deep learning.
about the author
is 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.