edujax_1.tar.gz

jaxbook

jax-code-widget

This course comprehensively introduces JAX and Flax, two open-source libraries that have gained prominence for their efficiency, flexibility, and scalability in deep learning applications. 

In this course, you’ll explore deep learning principles and understand the unique features of JAX and Flax. You will learn the basics of JAX, optimizers using JAX and Flax, and loss and activation functions. You’ll also learn how to load datasets, perform classification using distributed learning, and use ResNet and LSTM models. In the end, you will complete a project for hands-on experience using JAX and Flax for transfer learning. 

By the end of the course, you’ll be proficient in implementing and customizing neural network models using JAX and Flax, equipped with hands-on skills in advanced optimization and distributed training.

Deep Learning with JAX and Flax

# Flax and TensorFlow
Flax is the neural network library for JAX. TensorFlow is a deep learning library with a large ecosystem of tools and resources. Flax and TensorFlow are similar (for instance, they both can run on XLA) but different in some ways.

Let’s look at the differences between Flax and TensorFlow.


# Random number generation

In TensorFlow, we can set global or function level seeds. Generating random numbers in TensorFlow is quite straightforward.



Learn about the important functions in Flax and Tensorflow. 



Major Functions in Flax and TensorFlow

Learn about the important functions in Flax and Tensorflow.

Course Introduction

Basics of JAX

Optimizers in JAX and Flax

Loss and Activation Functions

Load Datasets in JAX

Image Classification and Distributed Training

TensorBoard and State Handling

LSTM in JAX and Flax

Flax vs. TensorFlow

Using ResNet Model in Flax

Transfer Learning in JAX and Flax

Conclusion

Appendix

Major Functions in Flax and TensorFlow

Flax and TensorFlow

Random number generation