Intermediate
62 Lessons
19h
Certificate of Completion
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This course includes
This course includes
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
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Course Content
Course Introduction
Basics of JAX
Optimizers in JAX and Flax
Loss and Activation Functions
Load Datasets in JAX
Image Classification and Distributed Training
6 Lessons
TensorBoard and State Handling
6 Lessons
LSTM in JAX and Flax
6 Lessons
Flax vs. TensorFlow
4 Lessons
Using ResNet Model in Flax
5 Lessons
Transfer Learning in JAX and Flax
Project
Conclusion
1 Lesson
Appendix
2 Lessons
Course Author
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Emma Bostian 🐞
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Evan Dunbar
ML Engineer
Carlos Matias La Borde
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Souvik Kundu
Front-end Developer
Vinay Krishnaiah
Software Developer
Eric Downs
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Kenan Eyvazov
DevOps Engineer
Anthony Walker
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Emma Bostian 🐞
@EmmaBostian
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