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Add Optimistix and NNX #108

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Add Optimistix and NNX #108

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2 changes: 1 addition & 1 deletion readme.md
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Expand Up @@ -36,10 +36,10 @@ This is a curated list of awesome JAX libraries, projects, and other resources.
- [EasyLM](https://github.com/young-geng/EasyLM) - LLMs made easy: Pre-training, finetuning, evaluating and serving LLMs in JAX/Flax. <img src="https://img.shields.io/github/stars/young-geng/EasyLM?style=social" align="center">
- [NumPyro](https://github.com/pyro-ppl/numpyro) - Probabilistic programming based on the Pyro library. <img src="https://img.shields.io/github/stars/pyro-ppl/numpyro?style=social" align="center">
- [Chex](https://github.com/deepmind/chex) - Utilities to write and test reliable JAX code. <img src="https://img.shields.io/github/stars/deepmind/chex?style=social" align="center">
- [Optax](https://github.com/deepmind/optax) - Gradient processing and optimization library. <img src="https://img.shields.io/github/stars/deepmind/optax?style=social" align="center">
- [RLax](https://github.com/deepmind/rlax) - Library for implementing reinforcement learning agents. <img src="https://img.shields.io/github/stars/deepmind/rlax?style=social" align="center">
- [JAX, M.D.](https://github.com/google/jax-md) - Accelerated, differential molecular dynamics. <img src="https://img.shields.io/github/stars/google/jax-md?style=social" align="center">
- [Coax](https://github.com/coax-dev/coax) - Turn RL papers into code, the easy way. <img src="https://img.shields.io/github/stars/coax-dev/coax?style=social" align="center">
- [Optax](https://github.com/deepmind/optax) - Gradient processing and optimization library. <img src="https://img.shields.io/github/stars/deepmind/optax?style=social" align="center">
- [Distrax](https://github.com/deepmind/distrax) - Reimplementation of TensorFlow Probability, containing probability distributions and bijectors. <img src="https://img.shields.io/github/stars/deepmind/distrax?style=social" align="center">
- [cvxpylayers](https://github.com/cvxgrp/cvxpylayers) - Construct differentiable convex optimization layers. <img src="https://img.shields.io/github/stars/cvxgrp/cvxpylayers?style=social" align="center">
- [TensorLy](https://github.com/tensorly/tensorly) - Tensor learning made simple. <img src="https://img.shields.io/github/stars/tensorly/tensorly?style=social" align="center">
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