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

# Share TensorBoard dashboards

TensorBoard.dev is a hosted version of TensorBoard that makes it easy to share our experiments. Let's upload TensorBoard to TensorBoard.dev.

```python
# On Colab or Jupyter notebook
!tensorboard dev upload --logdir ./runs \
  --name "Flax experiments" \
  --description "Logging model metrics with JAX" \
  --one_shot
```

When we run the code above, we will get a prompt to authorize the upload. We should be careful not to share sensitive data because TensorBoard.dev experiments are public.

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

Sharing TensorBoard

Share TensorBoard dashboards