JAX is a high performance numerical computing framework that brings together differentiation to Python code (Autograd) and Accelerated Linear Algebra (XLA) that compiles to low level, high performing code on accelerators, such as GPUs and TPUS. In this video, Laurence Moroney discusses what JAX is, what it does, and why it’s important in Google’s research efforts. Resources: JAX reference docs → GitHub repository→ Chapters: 0:00 - Intro 0:58 - Concepts to consider in ML frameworks 1:30 - What is the idea behind JAX? 2:33 - What are the main benefits of using JAX? 3:29 - Leave us questions in the comments! Watch more episodes of JAX Foundations → Subscribe to the Google Research Channel →