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Train, evaluate, and run machine learning models.

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7,594 projects
84stars
deepquantum preview

DeepQuantum for quantum computing

  • AI-Enhanced Quantum Computing Framework: Seamlessly integrated with PyTorch, it utilizes technologies such as automatic differentiation, vectorized parallelism, and GPU acceleration for efficiency. It facilitates the easy construction of hybrid quantum-classical models, enabling end-to-end training and inference.
  • User-Friendly API Design: The API is designed to be simple and intuitive, making it easy to initialize quantum neural networks and providing flexibility in data encoding.
Machine LearningJupyter NotebookApache-2.0GitHub

SST

tusen-ai

890stars

Code for a series of work in LiDAR perception, including SST (CVPR 22), FSD (NeurIPS 22), FSD++ (TPAMI 23), FSDv2, and CTRL (ICCV 23, oral).

Machine LearningPythonApache-2.0GitHub
634stars
LiteFlowNet preview

LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018 (Spotlight paper, 6.6%)

78stars

The spiritual successor to knockknock for PyTorch Lightning, get notified when your training ends

  • Supports E-Mail, Discord, Slack, Teams, Telegram

hlb-CIFAR10

tysam-code

1,318stars

Train to 94% on CIFAR-10 in <6.3 seconds on a single A100. Or ~95.79% in ~110 seconds (or less!)

Machine LearningPythonApache-2.0GitHub

afrolid

UBC-NLP

40stars
afrolid preview

AfroLID, a powerful neural toolkit for African languages identification which covers 517 African languages.

Machine LearningPythonApache-2.0GitHub

PPLM

uber-research

1,154stars
PPLM preview

Plug and Play Language Model implementation. Allows to steer topic and attributes of GPT-2 models.

Machine LearningPythonApache-2.0GitHub

orbit

uber

2,083stars
orbit preview

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

1,895stars

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

Machine LearningPythonApache-2.0GitHub