Skip to content
프로젝트 카테고리

머신러닝 · GitHub

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

유용한 GitHub 프로젝트

4,492 프로젝트

ICON

YuliangXiu

1,679스타

[CVPR'22] ICON: Implicit Clothed humans Obtained from Normals

RCAN

yulunzhang

1,515스타
RCAN preview

PyTorch code for our ECCV 2018 paper "Image Super-Resolution Using Very Deep Residual Channel Attention Networks"

lgo

yunabe

2,452스타
lgo preview

Interactive Go programming with Jupyter

  • Write and execute Go (golang) interactively like Python.
  • Jupyter Notebook integration
머신러닝GoBSD-3-ClauseGitHub

NeatRL

YuvrajSingh-mist

238스타

Readable single-file PyTorch implementations of deep RL algorithms

  • Single-file implementations: Each algorithm lives entirely in one self-contained file -- no hunting across modules
  • Experiment Tracking: Built-in support for Weights & Biases logging
머신러닝PythonApache-2.0GitHub
430스타

From-scratch PyTorch replications of classic and SOTA AI/ML papers: transformers, attention, GANs, diffusion, CLIP, LoRA, MoE, plus SFT/GRPO post-training experiments.

머신러닝Jupyter NotebookApache-2.0GitHub

PoinTr

yuxumin

873스타
PoinTr preview

[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

DDSP-SVC

yxlllc

2,713스타

Real-time end-to-end singing voice conversion system based on DDSP (Differentiable Digital Signal Processing)

pyod

yzhao062

10,086스타

표 형식, 시계열, 그래프, 텍스트, 이미지, 오디오 데이터의 이상 탐지를 위한 Python 라이브러리입니다. 60개 이상의 탐지기, 벤치마크로 뒷받침되는 ADEngine 오케스트레이션, AI 에이전트를 위한 에이전트형 워크플로를 제공합니다.

머신러닝PythonBSD-2-ClauseGitHub
2,562스타

An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at https://d2l.ai/.

머신러닝Jupyter NotebookApache-2.0GitHub

CogView

zai-org

1,800스타
CogView preview

Text-to-Image generation. The repo for NeurIPS 2021 paper "CogView: Mastering Text-to-Image Generation via Transformers".

머신러닝PythonApache-2.0GitHub

pytorch-ts

zalandoresearch

1,372스타
pytorch-ts preview

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

1,457스타
concrete-ml preview

Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.

  • Built-in models: Ready-to-use FHE-friendly models with a user interface that is equivalent to their the scikit-learn and XGBoost counterparts
  • Customs models: Concrete ML supports models that can use quantization-aware training. These are developed by the user using PyTorch or keras/tensorflow and are imported into Concrete ML through ONNX