Who uses JAX?

So, you know what JAX is and how it helped innovation beyond general purpose frameworks - optimizing them for accelerated math for ML and scientific computing purposes. In this episode of JAX Foundations, we give you a look at who uses this Python library, what they use it for, and its different use cases. Watch this video to learn if JAX is the right tool for your project!

Resources:

Chapters:
0:00 - Intro
0:24 - Who uses JAX and why?
0:35 - How is JAX useful for ML researchers?
0:50 - How does JAX help researchers with differentiation?
1:01 - How does JAX help researchers with vectorization?
1:36 - How JAX is useful for just-in-time compilation
1:54 - What models has JAX helped to create?
2:11 - What Google AI breakthroughs has JAX been responsible for?
2:53 - What is the goal JAX in research?

[Stwinky]

For you guys who haven’t tried it, Jax is totally worth the try. It’s a really natural extension of our dear friend numpy

[linkernick]

Could you explain in the forthcoming videos, how to work with random numbers in JAX in general and for e. g. network weights initialization or Monte-Carlo algorithm?

[sabaokangan]

Thank you so much for sharing this with us ❤️‍🔥

[wryltxw]

Are there ways to find roles that leverages the use of JAX?

[wryltxw]

I think separate videos on Lambda, Palm, and Pathways could be interesting.

[sabaokangan]

More JAX videos would be such a game changer

[wryltxw]

Also, separate videos on jaxfwd and jaxrev would be useful addition.