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

# "Concretization" type

The error occurs when we use a tracer object  _in lieu_ of the concrete. For example, using a tracer object in an `if-else` condition will throw an error similar to this:

>**ConcretizationTypeError**: Abstract tracer value encountered where concrete value is expected



# "Non-concrete" boolean index

The error is similar to the above error. One encounters it while trying to use non-concrete boolean indices. It will throw an error like this:

>**NonConcreteBooleanIndexError**: Array boolean indices must be concrete

# Tracer array conversion

We encounter this error while trying to use a tracer with either a non-JAX NumPy or NumPy arrays. It throws something like:

>**TracerArrayConversionError**: The `numpy.ndarray` conversion method was called on the JAX Tracer object

A summary of commonly occurring errors with tracer objects.

Introduction

JAX Programming Model

Linear Algebra

Random Variables and Distributions

JAX Ecosystem

Project: GAN Using the JAX ecosystem

Appendix

Common Tracer Errors

“Concretization” type