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

Which of the following options is not valid syntax for computing the inner product?

Which of the following options is not valid syntax for computing the inner product?

Which of the following functions is **not** a part of the **`linalg`** package?

Which of the following functions is not a part of the <code>linalg</code> package?

We cannot concatenate matrices using the function_______.

We cannot concatenate matrices using the function_______.

Which of the following functions is inconsistent with the **linear algebra** theory?

Which of the following functions is inconsistent with the linear algebra theory?

Vectors and Matrices

Vectors and Matrices

Let's test your knowledge of vectors and matrices in JAX with a quiz.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Quiz - Vectors and Matrices