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

These coding challenges test our familiarity with vectors and matrices operations respectively.


## Exercise 1 - Norm

The p-norm of a vector $x$ is defined as:

$$|x|^p =\sqrt[p] {x_1^p+x_2^p+x_3^p+...}$$

Take a vector $x$ and norm's order $p$ and implement its norm **without using the predefined function**.


These coding challenges test our familiarity with vectors and matrices operations respectively.


# Exercise 1 - Norm

The p-norm of a vector $x$ is defined as:

$$|x|^p =\sqrt[p] {x_1^p+x_2^p+x_3^p+...}$$

Take a vector $x$ and norm's order $p$ and implement its norm **without using the predefined function**.


Assess your understanding of vectors and matrices with a few programming exercises.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Challenge: Vectors and Matrices

Exercise 1 - Norm