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



This lesson assumes a certain level of familiarity with **NumPy**. Let's first start with how normal **NumPy** and JAX relate. 



# JAX Numpy

JAX has its own variant of **NumPy**, which we can import as:











One might worry that we have to re-learn *a new NumPy* from scratch, but luckily **the syntax of *NumPy* and JAX is the same**. For example:

This lesson will explore the relationship between JAX and NumPy, since JAX can be treated as a parallelized version of NumPy.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

JAX and NumPy

JAX Numpy