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

SGD implements **stochastic gradient descent** with support for momentum and #key# Nesterov acceleration: Nesterov acceleration refers to a method of accelerating the convergence of iterative optimization algorithms commonly used in machine learning. #key#. Momentum makes obtaining optimal model weights faster by accelerating gradient descent in a certain direction.

import optax
seed = random.PRNGKey(0)
learning_rate = jnp.array(1/1e4)

model = CNN()
weights = model.init(seed, X_train[:5])

optimizer = optax.sgd(learning_rate=learning_rate) # Initialize SGD as Optimizer
optimizer_state = optimizer.init(weights) # Optmizer state

Learn about SGD-based optimizers in JAX and Flax.

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

Stochastic Gradient Descent