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
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Content
1.
Course Introduction
1 Lessons
Get familiar with JAX and Flax libraries for high-performance machine learning.
2.
Basics of JAX
9 Lessons
Discover how JAX optimizes machine learning with JIT compilation, pure functions, and advanced differentiation.
3.
Optimizers in JAX and Flax
7 Lessons
Work your way through optimizer selection, training, and performance analysis in JAX and Flax.
4.
Loss and Activation Functions
8 Lessons
Break down the steps to implement loss and activation functions using JAX for neural networks.
5.
Load Datasets in JAX
7 Lessons
Solve problems in dataset loading, preprocessing, and model training using JAX and TensorFlow.
6.
Image Classification and Distributed Training
6 Lessons
Simplify complex topics of image classification and distributed training using JAX and Flax.
7.
TensorBoard and State Handling
6 Lessons
Master TensorBoard integration, logging metrics, and managing training states with JAX and Flax.
8.
LSTM in JAX and Flax
6 Lessons
Sharpen your skills in preprocessing text data and building LSTM models with JAX and Flax.
9.
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.
11.
Conclusion
1 Lessons
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.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Course Author:
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