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머신러닝 · GitHub

모델 학습, 평가, 추론 프레임워크를 찾고 커뮤니티의 관심도를 비교하세요.

유용한 GitHub 프로젝트

7,572 프로젝트

DeepGBM

motefly

658스타

SIGKDD'2019: DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks

ModelsGenesis

MrGiovanni

795스타

[MICCAI 2019 Young Scientist Award] [MedIA Best Paper Award] Models Genesis: self-supervised pre-training for 3D medical images. Learns transferable representations from unlabeled CT and MRI volumes, then fine-tunes for downstream segmentation and classification. Keras and PyTorch weights included.

머신러닝Jupyter NotebookGitHub
948스타
calculate-flops.pytorch preview

The calflops is designed to calculate FLOPs、MACs and Parameters in all various neural networks, such as Linear、 CNN、 RNN、 GCN、Transformer(Bert、LlaMA etc Large Language Model)

Msnhnet

msnh2012

737스타

🔥 (yolov3 yolov4 yolov5 unet ...)A mini pytorch inference framework which inspired from darknet.

freeflow

msu-video-group

29스타

[ECCV'26] FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation

neural-ode

msurtsukov

804스타

Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations

머신러닝Jupyter NotebookGitHub

SimRepo

Mubelotix

468스타
SimRepo preview

Better Github Star History charts, and repository recommendation engine

  • Doesn't require API tokens;
  • Discover similar repositories to find the best option for your needs.

cadence

muellerberndt

625스타
cadence preview

A brain that learns from live experience. System 1 by default: perception, memory, plasticity and action. Optional System 2 for self-observation and slow corrections.

머신러닝PythonGPL-3.0GitHub

SEAL

muhanzhang

677스타

SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction). "M. Zhang, Y. Chen, Link Prediction Based on Graph Neural Networks, NeurIPS 2018 spotlight".

rs-trafilatura

Murrough-Foley

70스타

Fast, accurate web content extraction in Rust. ML page-type classification, per-type extraction, confidence scoring. F1=0.966 on ScrapingHub (#1), F1=0.859 across 2,008 annotated pages (1,497 development + 511 held-out test

  • Fast: 71 files/s for articles, 46 files/s overall on a 1,497-page benchmark (pure Rust, compile-time regex)
  • Accurate: F1 0.966 on ScrapingHub benchmark, F1 0.859 across 7 page types
머신러닝RustApache-2.0GitHub