HomeCoursesDeep Learning with JAX and Flax

Intermediate

19h

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.

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Overview

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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.

This course comprehensively introduces JAX and Flax, two open-source libraries that have gained prominence for their efficiency,...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

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

Content

62 Lessons1 Project9 Quizzes

Course Introduction

1 Lessons

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

About the Course

Basics of JAX

9 Lessons

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.

Introduction to Optimizers

Adaptive Optimizers

Stochastic Gradient Descent

Challenge: Optimizers

Solution: Optimizers

Summary: Optimizers in JAX and Flax

Quiz: Optimizers

Loss and Activation Functions

8 Lessons

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

7 Lessons

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.

Image Classification with JAX and Flax

Distributed Training with JAX and Flax

Challenge: Distributed Training with JAX and Flax

Solution: Distributed Training with JAX and Flax

Summary: Image Classification and Distributed Training

Quiz: Distributed Training with JAX and Flax

TensorBoard and State Handling

6 Lessons

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

Setting Up TensorBoard

How to Use TensorBoard with Flax

Track Model Training in JAX Using TensorBoard

Applying BatchNorm and DropOut Layers in JAX and Flax

Summary: TensorBoard and State Handling

Quiz: TensorBoard and State Handling

LSTM in JAX and Flax

6 Lessons

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

Data Preprocessing

LSTM Model

Challenge: LSTM in JAX and Flax

Solution: LSTM in JAX and Flax

Summary: LSTM in JAX and Flax

Quiz: LSTM in JAX and Flax

Flax vs. TensorFlow

4 Lessons

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

Major Functions in Flax and TensorFlow

Training model in Flax vs. TensorFlow

Summary: Flax vs. TensorFlow

Quiz: Flax vs. TensorFlow

10.

Using ResNet Model in Flax

5 Lessons

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

Training the ResNet Model

ResNet Model Definition

Transfer Learning using ResNet Model

Summary: Using ResNet Model in Flax

Quiz: Using ResNet Model in Flax

Transfer Learning in JAX and Flax

Project

11.

Conclusion

1 Lessons

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.

Installing Packages

Sharing TensorBoard

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Derrick Mwiti

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