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

# Functional programming (FP)


**Functional programming** is a paradigm focused on functions. It's okay if you are unfamiliar with functional programming because JAX mainly centers around just one of its key features.


# Pure functions

**Pure functions** are similar to mathematical functions and have the following properties:

1. **Identical outputs**: No matter how many times we run the function, it gives the same output for the same tuple of inputs.
2. **No side-effects**: The function doesn't change any variable other than variables in its local scope.

## Examples

Here is a basic example of a pure function:




Functional programming is the central theme of JAX. This lesson will shed some light on what exactly this means.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Pure Functions

Functional programming (FP)