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

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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

# Sigmoid binary cross entropy

The sigmoid binary cross entropy loss is computed using `optax.sigmoid_binary_cross_entropy`. The function expects logits and class labels. It is used in problems where the classes are not mutually exclusive. For example, the model can predict that the image contains two objects in an image classification problem.


import optax

print(optax.sigmoid_binary_cross_entropy(0.5,0.0))

# The softmax cross-entropy function

The `softmax_cross_entropy()` function is used where the classes are mutually exclusive. For example, in the MNIST dataset, each digit has exactly one label. The function expects an array of logits and probability distributions. The probability distribution sums to 1.


Learn about various loss functions of the Optax package.

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

Types of Loss Functions in JAX

Sigmoid binary cross entropy

The softmax cross-entropy function