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

## Introduction

Those that are familiar with **stochastic gradient descent** (SGD) will know that it is applied one sample at a time, thus making it computationally inefficient. Instead, we use it in the batches in a technique usually known as **minibatch gradient descent**. 

This batching operation is a common practice throughout the deep learning regime and can be used for various tasks like convolution, optimization, and so on. 

Let's have a look at a convolution function for 1D vectors:



# Introduction

Those that are familiar with **stochastic gradient descent** (SGD) will know that it is applied one sample at a time, thus making it computationally inefficient. Instead, we use it in the batches in a technique usually known as **minibatch gradient descent**. 

This batching operation is a common practice throughout the deep learning regime and can be used for various tasks like convolution, optimization, and so on. 

Let's have a look at a convolution function for 1D vectors:



This lesson will provide an introduction to auto vectorization in JAX.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Auto Vectorization

Introduction