Gain insights into JAX and Flax's features for deep learning. Learn about optimizers, functions, data loading, and model training. Explore hands-on projects for practical experience.

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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

# Pure functions

A **pure function** has no side effects and expects the output to come only from its inputs. JAX transformation functions expect pure functions.
When working with JAX, all input should be passed through function parameters, while all output should come from the function results. Hence, something like Python's `print` function is not pure. This can be demonstrated using the `impure_print_side_effect()` function in the code example.

The side effects appear during the first run. Subsequent runs with parameters of the same type and shape may not show the side effects. This is because JAX now invokes a cached compilation of the function. JAX reruns the Python function when the type or shape of the argument changes.



Learn how to generate random numbers and pure 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

Pure Functions and Random Numbers

Pure functions