Discover the power of JAX in deep learning. Gain insights into its ecosystem and learn about linear algebra, pseudo-random number generation, and optimization algorithms for cleaner, structured coding.
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Overview
Content
Reviews
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 skills that will make deep learning programming more intuitive, structured, and clean.
JAX is a Python library designed for high-performance ML research. It is a powerful numerical computing library, just like Numpy...Show More
WHAT YOU'LL LEARN
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
Learn the basics of JAX
Show more
TAKEAWAY SKILLS
Random Variables
Neural Networks
Functional Programming
Deep learning basics
Content
53 Lessons1 Project7 Quizzes14 Challenges
1.
Introduction
2 Lessons
Get familiar with JAX, a powerful library for deep learning and numerical computing.
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