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

**(True or False)** A loss function is also referred to as a cost function. 

(True or False) A loss function is also referred to as a cost function.

Given a problem where the classes are not mutually exclusive, which loss function should we apply?

Given a problem where the classes are not mutually exclusive, which loss function should we apply?

You are given the task of predicting digits in an image, where each image has exactly one digit. Which loss function should you use?

You are given the task of predicting digits in an image, where each image has exactly one digit. Which loss function should you use?

**(Select all that apply.)** Which are ways of monitoring loss in JAX? 

(Select all that apply.) Which are ways of monitoring loss in JAX?

**(Select all that apply.)** Which leads to NaN in the loss in JAX?

(Select all that apply.) Which leads to NaN in the loss in JAX?

Which activation function caps the output to zero and above and is mainly used in hidden layers?

Which activation function caps the output to zero and above and is mainly used in hidden layers?

Which activation function is used in multi-class problems where the classes are mutually exclusive? 


Which activation function is used in multi-class problems where the classes are mutually exclusive?

CELU—Continuously-differentiable Exponential Linear Unit


Which activation function would you choose to deal with the vanishing gradient problem?

Which activation function would you choose to deal with the vanishing gradient problem?

Which activation function caps its output to -1 and 1?

Which activation function caps its output to -1 and 1?

Which ​​activation function is a smooth version of ReLu?

Which ​​activation function is a smooth version of ReLu?

Let's have a short quiz to test your understanding.

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: Loss and Activation Functions