🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
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Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Convert Machine Learning Code Between Frameworks
Low-code framework for building custom LLMs, neural networks, and other AI models
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
Superduper: End-to-end framework for building custom AI applications and agents.
An open source library and framework for deep learning on satellite and aerial imagery.
An autoML framework & toolkit for machine learning on graphs.
allRank is a framework for training learning-to-rank neural models based on PyTorch.
TensorLayerX: A Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Backends and OS.
Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.