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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

> Haiku is a tool <div>
For building neural networks <div>
Think: "Sonnet for JAX"

## Introduction

If we check the official documentation of the **Haiku**, we will find the above _literal haiku_. 

 Like Keras or Sonnet for Tensorflow, **Haiku** is a high-level library that provides an **object-oriented** interface for building neural networks (in JAX).

As a starter, let's use a two-layer MLP.

 


> Haiku is a tool <div>
For building neural networks <div>
Think: "Sonnet for JAX"

# Introduction

If we check the official documentation of the **Haiku**, we will find the above _literal haiku_. 

 Like Keras or Sonnet for Tensorflow, **Haiku** is a high-level library that provides an **object-oriented** interface for building neural networks (in JAX).

As a starter, let's use a two-layer MLP.

 


This lesson will introduce Haiku, a high-level neural network library that provides an object-oriented interface. 

Haiku

This lesson will introduce Haiku, a high-level neural network library that provides an object-oriented interface.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Haiku

Introduction