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

# How fast is JAX?

JAX uses **asynchronous dispatch**, meaning it does not wait for computation to complete to give control back to the Python program. Therefore, when we perform an execution, JAX will return a future. JAX forces Python to wait for the execution when we want to print the output or convert the result to a NumPy array.

Therefore, if we want to compute the time of execution of a program, we’ll have to convert the result to a NumPy array using `block_until_ready()` to wait for the execution to complete. Generally speaking, NumPy will outperform JAX on the CPU, but JAX will outperform NumPy on accelerators and when using jitted functions.






Learn about ways to accelerate functions in JAX.

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

Just-in-Time Compilation

How fast is JAX?