JAX

Python library for composable transformations, automatic differentiation, and accelerated array computing.

Scientific Python Libraries Advanced Accelerated Computing Library Explore Provisional Not currently taught
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Explore Provisional Not currently taught

JAX is useful for high-performance numerical work that benefits from compilation, vectorization, and automatic differentiation.

Provisional positions are editorial proposals awaiting group review.

Not currently taught — Relevant to the topic landscape; no explicit recorded offering is currently attributed to this item.

0 topics with recorded teaching · 0 past offerings · 0 upcoming offerings

Related themes: Scientific Computing

Related topics: Arrays and Numerical ComputingMachine Learning

Common use cases

  • Accelerated array computing
  • Automatic differentiation
  • Machine learning research

Getting started

JAX supports compiled numerical computing and automatic differentiation for research workflows that need both flexibility and speed.