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



For two functions, _f_ and _g_, **convolution** (represented by __*__) is a mathematical operation expressing **how the shape of *f* is modified by *g***. 

$$(f * g)(t) := \int_{-\infty}^\infty f(\tau) g(t - \tau) \, d\tau$$

# Example



As an example, we have $f(x)$ as a sine wave:

$$f(x) = sin(x); x\in(-2\pi,2\pi)$$

Similarly, $g(x)$ is **cosine function** defined from $(-2\pi,2\pi)$

$$g(x) = cos(x); x\in(-2\pi,2\pi)$$

After their convolution, we get the following result (_in green_). Inevitably, there is an increase in the domain due to $-\infty$ to $+\infty$ integration as well as range (multiplication between the two signals).


This lesson will introduce convolutions in JAX.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Convolutions