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.

Doxx.tar.gz

NormalCode

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


## Exercise 1: PRNG

The key component of developing any random variables setup is having a good grip on PRNG. Begin by creating a PRNG with a seed of, say, 100, and create a subkey $-$ including regeneration of existing key $-$ from the main key.




# Exercise 1: PRNG

The key component of developing any random variables setup is having a good grip on PRNG. Begin by creating a PRNG with a seed of, say, 100, and create a subkey $-$ including regeneration of existing key $-$ from the main key.



Let's test our learning of PRNG and probability distributions through a coding challenge.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Challenge: PRNG and Probability Distribution

Exercise 1: PRNG