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

To enable the object-oriented Haiku code to convert into pure functions

To project the inputs into higher dimensions.

Why do we use <code>Haiku.transform()</code>?

Which of the following options is not an argument of Adam optimizer?

Which of the following options is not an argument of Adam optimizer?

Gradient clipping is used for (multiple options allowed):

Gradient clipping is used for (multiple options allowed):

What is the graph datatype in Jraph?

What will be the outcome of **`assert_devices_available(1,'cpu') `** on a multicore machine (without any GPU)?

What will be the outcome of <code>assert_devices_available(1,'cpu') </code> on a multicore machine (without any GPU)?

Let's test our knowledge about the JAX ecosystem.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Quiz: The JAX Ecosystem