GitHub - google/jax: Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more - GitHub - google/jax: Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

jax.numpy.interp has poor performance on TPUs · Issue #16182 · google/jax · GitHub

谷歌开源计算框架JAX：比NumPy快30倍，还可在TPU上运行- 知乎

10 Python Libraries for Machine Learning You Should Try Out in 2023!

GitHub - jax-md/jax-md: Differentiable, Hardware Accelerated, Molecular Dynamics

JAX performance can be better (comparison with PyTorch) · Issue #1832 · google/jax · GitHub

Learning JAX in 2023: Part 1 — The Ultimate Guide to Accelerating Numerical Computation and Machine Learning - PyImageSearch

BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming

Google Open Source

D] Should We Be Using JAX in 2022? : r/MachineLearning

High-level GPU code: a case study examining JAX and OpenMP.

Learn how to leverage JAX and TPUs to train neural networks at a significantly faster speed