4.5
Intermediate
2h 30min
Introduction to JAX and Deep Learning
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.
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
Content
1.
Introduction
2 Lessons
Get familiar with JAX, a powerful library for deep learning and numerical computing.
2.
JAX Programming Model
9 Lessons
Walk through JAX's programming model, including pure functions, JIT, jaxpr, and autodiff.
3.
Linear Algebra
15 Lessons
Explore the fundamental concepts of vectors, matrices, multivariate calculus, and convolutions in deep learning.
4.
Random Variables and Distributions
7 Lessons
Grasp the fundamentals of random variables, distributions, PRNGs, and divergence measures in JAX.
5.
JAX Ecosystem
14 Lessons
Take a closer look at the tools and libraries within the JAX ecosystem for deep learning.
6.
Appendix
6 Lessons
Focus on installation steps, notable JAX libraries, models, vector calculus, common errors, and key terms.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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