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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.

5.0

62 Lessons

19h

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LEARNING OBJECTIVES

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

Learning Roadmap

62 Lessons1 Project9 Quizzes

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

7 Lessons

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

Loss and Activation Functions

8 Lessons

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

Load Datasets in JAX

7 Lessons

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

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

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

Wrap Up

12.

Appendix

2 Lessons

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

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

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.

ABOUT THE AUTHOR

Derrick Mwiti

I am experienced in data science, machine learning, and deep learning with a keen eye for building machine learning communities.

Learn more about Derrick

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