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

**(True or False)** JAX doesn’t ship with data loading utilities.

(True or False) JAX doesn’t ship with data loading utilities.

**(Select all that apply.)** What can we use to load datasets in JAX?

(Select all that apply.) What can we use to load datasets in JAX?

Which library provides stop words?

Which function is used to convert the text data into integer representations? 


Which function is used to convert the text data into integer representations?

**(Fill in the blank.)** A training state is required before training the model. This is because JAX expects ________ functions.

(Fill in the blank.) A training state is required before training the model. This is because JAX expects ________ functions.

Test the concepts you have learned so far.

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

Quiz: Loading Datasets in JAX