Deep Learning with JAX and Flax

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.

Intermediate

62 Lessons

19h

Certificate of Completion

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.

AI-POWERED

Explanations

AI-POWERED

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This course includes

1 Project

104 Playgrounds

9 Quizzes

This course includes

1 Project

104 Playgrounds

9 Quizzes

Course Overview

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 LST...Show More

What You'll Learn

An understanding of the basics of JAX, including Autograd and array operations

The ability to apply JAX for numerical computing and machine learning tasks

Hands-on experience using the Flax framework for defining, customizing, and training neural network architectures

The ability to apply and adjust learning rates for various optimizers available in JAX and Flax

Hands-on experience performing training in a distributed computing environment

The ability to apply ResNet and LSTM models along with transfer learning using JAX and Flax

What You'll Learn

An understanding of the basics of JAX, including Autograd and array operations

Course Content

Course Introduction

Get familiar with JAX and Flax libraries for high-performance machine learning.

About the Course

Basics of JAX

Discover how JAX optimizes machine learning with JIT compilation, pure functions, and advanced differentiation.

Introduction to JAX Data Types and Array Pure Functions and Random Numbers Array Operations Just-in-Time Compilation Machine Learning with JAX Challenge: Basics of JAX Solution: Basics of JAX Summary: Basics of JAX Quiz: Basics of JAX

Optimizers in JAX and Flax

Work your way through optimizer selection, training, and performance analysis in JAX and Flax.

Introduction to Optimizers Adaptive Optimizers Stochastic Gradient Descent Challenge: Optimizers Solution: Optimizers Summary: Optimizers in JAX and Flax Quiz: Optimizers

Loss and Activation Functions

Break down the steps to implement loss and activation functions using JAX for neural networks.

Introduction to Loss Functions Types of Loss Functions in JAX Computing and Monitoring Loss in JAX Activation Functions in JAX Challenge: Loss and Activation function Solution: Loss and Activation function Summary: Loss and Activation Functions Quiz: Loss and Activation Functions

Load Datasets in JAX

Solve problems in dataset loading, preprocessing, and model training using JAX and TensorFlow.

Preparing Text Dataset Text Preprocessing and Sentiment Analysis Loading Image Dataset Challenge: Train and Test a Model Solution: Train and Test a Model Summary: Load Datasets in JAX Quiz: Loading Datasets in JAX

Image Classification and Distributed Training

6 Lessons

Simplify complex topics of image classification and distributed training using JAX and Flax.

TensorBoard and State Handling

6 Lessons

Master TensorBoard integration, logging metrics, and managing training states with JAX and Flax.

LSTM in JAX and Flax

6 Lessons

Sharpen your skills in preprocessing text data and building LSTM models with JAX and Flax.

Flax vs. TensorFlow

4 Lessons

Unpack the core of the critical differences between Flax and TensorFlow for deep learning.

10.

Using ResNet Model in Flax

5 Lessons

Work your way through training, defining, and fine-tuning a ResNet model in Flax.

Transfer Learning in JAX and Flax

Project

11.

Conclusion

1 Lesson

Grasp the fundamentals of JAX, Flax libraries, LSTM, ResNet, and distributed training.

12.

Appendix

2 Lessons

Take a look at installing, using JAX and Flax packages, and sharing TensorBoard experiments.

Course Author

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