Tags / Machine learning /
Deep learning framework
A library to define, train, and run neural networks, such as PyTorch, TensorFlow, or JAX.
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readme:
TensorFlow is an end-to-end open source platform for machine learning.
readme:
Transformers acts as the model-definition framework for state-of-the-art machine learning with text, computer vision, audio, video, and multimodal models, for both inference and training.
- pytorch/pytorch105K
readme:
Deep neural networks built on a tape-based autograd system
readme:
Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only).
- jax-ml/jax37K
readme:
JAX is a Python library for accelerator-oriented array computation and program transformation,
- BVLC/caffe35K
readme:
Caffe is a deep learning framework made with expression, speed, and modularity in mind.
readme:
tinygrad is an end-to-end deep learning stack:
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MAX AI framework: /max/python/max (model authoring in Python, since November 2025)
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MLX has higher-level packages like `mlx.nn` and `mlx.optimizers` with APIs that closely follow PyTorch to simplify building more complex models.
readme:
fastai is a deep learning library which provides practitioners with high-level components
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