从 65,720 个高星开源项目里，
找到你要的下一个。

100-Days-Of-ML-Code中文版

100-Days-Of-ML-Code中文版

stanford-cs-229-machine-learning 是一个学习资料，用于系统学习与查阅。

VIP cheatsheets for Stanford's CS 229 Machine Learning

awesome-artificial-intelligence 是用 Python 编写的学习资料，用于系统学习与查阅。

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.

anomalib 是用 Python 编写的学习资料，用于系统学习与查阅。

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and ed…

mlxtend 是用 Python 编写的学习资料，用于系统学习与查阅。

A library of extension and helper modules for Python's data analysis and machine learning libraries.

simclr 是用 Jupyter Notebook 编写的学习资料，用于系统学习与查阅。

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

mmselfsup 是用 Python 编写的学习资料，用于系统学习与查阅。

OpenMMLab Self-Supervised Learning Toolbox and Benchmark

voxelmorph 是用 Python 编写的学习资料，用于系统学习与查阅。

Unsupervised Learning for Image Registration

SimCLR 是用 Jupyter Notebook 编写的学习资料，用于系统学习与查阅，主打易用。

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

opencog 是用 Scheme 编写的学习资料，用于系统学习与查阅。

A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)

SfMLearner 是用 Jupyter Notebook 编写的学习资料，用于系统学习与查阅。

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

awesome-transfer-learning 是一个学习资料，用于系统学习与查阅。

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

minisom 是用 Python 编写的学习资料，用于系统学习与查阅。

:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps

Unsupervised-Classification 是用 Python 编写的学习资料，用于系统学习与查阅。

SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

awesome-graph-self-supervised-learning 是一个学习资料，用于系统学习与查阅。

Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"

Orion 是用 Python 编写的学习资料，用于系统学习与查阅。

Unsupervised time series anomaly detection library

awesome-contrastive-self-supervised-learning 是一个学习资料，用于系统学习与查阅，主打功能完整。

A comprehensive list of awesome contrastive self-supervised learning papers.

variational-autoencoder 是用 Python 编写的 AI 工具，用于人工智能方向的建模与推理，主打实现清晰。

Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

unsup3d 是用 Python 编写的学习资料，用于系统学习与查阅。

(CVPR'20 Oral) Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

HyperGAN 是用 Python 编写的学习资料，用于系统学习与查阅。

Composable GAN framework with api and user interface

autovc 是用 Python 编写的学习资料，用于系统学习与查阅。

AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

EnlightenGAN 是用 Python 编写的学习资料，用于系统学习与查阅。

[IEEE TIP] "EnlightenGAN: Deep Light Enhancement without Paired Supervision" by Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen…

DiscoGAN-pytorch 是用 Jupyter Notebook 编写的学习资料，用于系统学习与查阅。

PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

One-Pot_Multi-Frame_Denoising 是用 Python 编写的学习资料，用于系统学习与查阅。

The official implementation of IJCV & BMVC 2022 paper "One-Pot Multi-frame Denoising".