JAX
DeepMind's functional programming library for neural networks; emerging favorite for research in mechanistic interpretability and novel architectures.
Think of it like
The mathematician's choice; built on composable functional transformations.
Example
JAX code emphasizes immutability and functional composition; `jax.vmap` and `jax.grad` compose easily.
How it actually works
JAX is based on functional primitives (vmap, pmap, jit). It enables powerful metaprogramming and efficient compilation. Smaller ecosystem than PyTorch but growing fast.
For product teams
Used by cutting-edge research labs (DeepMind, OpenAI); smaller production footprint.
For engineers
JAX is powerful for research; productionizing JAX is still an open question.
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