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

At this point in the course, JAX doesn't seem much like a specialized neural network library, despite its wide suite of features. This is understandable given JAX, at its core, is a parallelizable NumPy with a number of other features.

The story doesn’t conclude there, though. In order to facilitate deep learning, JAX has its own **ecosystem** of libraries, comprising of:

1. Haiku
2. Optax
3. Jraph
4. RLax 
5. Chex

Each of these libraries deserves a chapter of its own, and even that would be unable to fully unpack their particular details. However, we’ll just provide a brief overview of each in this introductory course. 

>**Note:** Flax is a library for neural networks but is usually not considered part of the JAX ecosystem.





This chapter provides an overview of the JAX Ecosystem.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Introduction to JAX Ecosystem