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


## Important points

Before solving the exercises, please make sure to:

* Do _not_ change the name of the function below because it will be used by the evaluator.
* Do _not_ print anything. 
* Do _not_ import any libraries. They are pre-imported. We'll continue the convention of **`jnp`** for JAX NumPy.
* Variables are usually initialized with $1$ to help run the test case. Please **override them with intended expressions/values** in your implementation.

>**Note:** These points will also apply to all the subsequent challenges.

## Exercise 1: Testing array for zeros

Let's test our familiarity with the basic JAX and NumPy syntax.

In this simple exercise, we are given a JAX array `a`. Identify whether it contains $0$ or not. In other words, if it contains an(y) instance of zero, it should return **`true`** and vice-versa.




# Important points

Before solving the exercises, please make sure to:

* Do _not_ change the name of the function below because it will be used by the evaluator.
* Do _not_ print anything. 
* Do _not_ import any libraries. They are pre-imported. We'll continue the convention of **`jnp`** for JAX NumPy.
* Variables are usually initialized with $1$ to help run the test case. Please **override them with intended expressions/values** in your implementation.

>**Note:** These points will also apply to all the subsequent challenges.

# Exercise 1: Testing array for zeros

Let's test our familiarity with the basic JAX and NumPy syntax.

In this simple exercise, we are given a JAX array `a`. Identify whether it contains $0$ or not. In other words, if it contains an(y) instance of zero, it should return **`true`** and vice-versa.



Test your learning so far by completing a programming exercise.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Challenge: Basics of JAX

Important points

Exercise 1: Testing array for zeros