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audit: sweep Deep Learning, re-tier frameworks

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The tensorflow line called it "the most popular Deep Learning framework", false by downloads (torch 59.8M/month vs tensorflow 11.4M on pypistats, 2026-10-02), and the Frameworks tiers no longer matched the evidence: tensorflow 2.21.0 still ships no cp314 wheels (tensorflow/tensorflow#102890) and its releases now focus on security and bug fixes, while keras's 11.76M is mostly tensorflow pulling it in (requires keras>=3.12.0). Both move to Second Tier; jax (8.0M, Google's own successor) and pytorch-lightning (10.1M) become obvious choices.

Co-Authored-By: Claude <noreply@anthropic.com>
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Vinta Chen
2026-10-02 11:37:22 +08:00
co-authored by Claude
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@@ -188,10 +188,10 @@ _Frameworks for Neural Networks and Deep Learning. Also see [awesome-deep-learni
- Frameworks
- [pytorch](https://github.com/pytorch/pytorch) - Tensors and Dynamic neural networks in Python with strong GPU acceleration.
- [tensorflow](https://github.com/tensorflow/tensorflow) - The most popular Deep Learning framework created by Google.
- [keras](https://github.com/keras-team/keras) - A high-level deep learning library with support for JAX, TensorFlow, and PyTorch backends.
- [jax](https://github.com/jax-ml/jax) - A library for high-performance numerical computing with automatic differentiation and JIT compilation.
- [pytorch-lightning](https://github.com/Lightning-AI/pytorch-lightning) - Deep learning framework to train, deploy, and ship AI products Lightning fast.
- [jax](https://github.com/jax-ml/jax) - A library for high-performance numerical computing with automatic differentiation and JIT compilation.
- [keras](https://github.com/keras-team/keras) - A high-level deep learning library with support for JAX, TensorFlow, and PyTorch backends.
- [tensorflow](https://github.com/tensorflow/tensorflow) - An end-to-end machine learning platform from Google.
- Reinforcement Learning
- [gymnasium](https://github.com/Farama-Foundation/Gymnasium) - A standard API for reinforcement learning environments with popular reference environments ([gym](https://github.com/openai/gym) successor).
- [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) - PyTorch implementations of Stable Baselines (deep) reinforcement learning algorithms.