Discover the power of JAX in deep learning. Gain insights into its ecosystem and learn about linear algebra, pseudo-random number generation, and optimization algorithms for cleaner, structured coding.

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JuPyter

JAX is a Python library designed for high-performance ML research. It is a powerful numerical computing library, just like Numpy, but with some key improvements.

In this course, you will learn all about JAX and its ecosystem of libraries (Haiku, Jraph, Chex, Flax, Optax). Addressing a wide range of audiences, you will cover several topics including linear algebra, random variables theory, pseudo-random number generation, and optimization algorithms.

By the end of this course, you will have a new set of skills that will make deep learning programming more intuitive, structured, and clean.

Introduction to JAX and Deep Learning

With ever-increasing data and computational resources, there is always a pressing need for parallel processing. Luckily, JAX facilitates this feature as well. Just like `vmap()`, we can use **`pmap()`** for parallel execution of a given function. 

>**Remember**: Since this lesson's subjects, `vmap()` and `pmap()`, require GPU/TPU support, the code snippets are provided just as a guide here. Executing them on a normal, non-GPU, machine will result in an error!


This lesson will introduce auto parallelization in JAX.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Auto Parallelization