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.

edujax_1.tar.gz

jaxbook

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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

# Computing loss with JAX metrics

**JAX metrics** is an open-source package for computing losses and metrics in JAX. It provides a Keras-like API for computing model loss and metrics. For example, here is how we use the library to compute the cross entropy loss. 


import jax_metrics as jm
crossentropy = jm.losses.Crossentropy()

logits = jnp.array([0.50, 0.60, 0.70, 0.30, 0.25])
labels = jnp.array([0.0, 1.0, 1.0, 0.0, 0.0])

print(crossentropy(target=labels, preds=logits))
print(jm.losses.crossentropy(target=labels, preds=logits))

Here is what the code would look like in a JAX training step:

Learn about various ways to calculate and monitor loss in models using 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

Computing and Monitoring Loss in JAX

Computing loss with JAX metrics