Introduction to JAX and Deep Learning
Intermediate
53 Lessons
2h 30min
Certificate of Completion
Takeaway Skills
Learn the basics of JAX
Learn how to apply Autograd
Use auto vectorization for batching
Use Haiku and Flax for implementing neural networks
Cover Optax and overview of common optimization algorithms in deep learning
Use Chex for testing JAX programs
Learn the basics of applied linear algebra
Learn random variables theory and probability distributions
Learn pseudo-random number generation
Cover the basics of optimal transport
Course Overview
JAX is a Python library designed for high-performance ML research. It is a powerful numerical computing library, just like Numpy, but with some key improvements. In this course, you will learn all about JAX and its ecosystem of libraries (Haiku, Jraph, Chex, Flax, Optax). Addressing a wide range of audiences, you will cover several topics including linear algebra, random variables theory, pseudo-random number generation, and optimization algorithms. By the end of this course, you will have a new set of sk...Show More
Course Content
Introduction
JAX Programming Model
Linear Algebra
Random Variables and Distributions
JAX Ecosystem
Project: GAN Using the JAX ecosystem
Project
Appendix
6 Lessons
How You'll Learn
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