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:
JAX reference docs → https://goo.gle/3IE4UQ7
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?
Watch more episodes of JAX Foundations → https://goo.gle/JAXFoundations
Subscribe to the Google Research Channel → https://goo.gle/GoogleResearch