Deep Learning with JAX and Flax
INTERACTIVE COURSE

Deep Learning with JAX and Flax

Intermediate

62 Lessons

19h

Certificate of Completion

AI Explanations
AI Explanations
Deep Learning with JAX and Flax
1 Project
104 Playgrounds
9 Quizzes
52 Illustrations

Takeaway Skills

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

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

Course Content

1

Course Introduction

2

Basics of JAX

3

Optimizers in JAX and Flax

4

Loss and Activation Functions

5

Load Datasets in JAX

6

Image Classification and Distributed Training

6 Lessons

7

TensorBoard and State Handling

6 Lessons

8

LSTM in JAX and Flax

6 Lessons

9

Flax vs. TensorFlow

4 Lessons

10

Using ResNet Model in Flax

5 Lessons

Transfer Learning in JAX and Flax

Project

11

Conclusion

1 Lesson

12

Appendix

2 Lessons

COURSE AUTHOR

How You'll Learn

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Progress You Can Show

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