#Unsupervised-learning
Showing 60 of 116 repositories tagged #unsupervised-learning, ranked by stars
100-Days-Of-ML-Code中文版
VIP cheatsheets for Stanford's CS 229 Machine Learning
A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
A library of extension and helper modules for Python's data analysis and machine learning libraries.
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
Unsupervised Learning for Image Registration
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
A curated list of community detection research papers with implementations.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
🚀 FREE AI Resources - 🎓 Courses, 👷 Jobs, 📝 Blogs, 🔬 AI Research, and many more - for everyone!
:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"
Unsupervised time series anomaly detection library
Composable GAN framework with api and user interface
A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs
A review of change detection methods, including codes and open data sets for deep learning. From paper: change detection based on artificial intelligence: state-of-the-art and challenges.
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Experiments for understanding disentanglement in VAE latent representations
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN
Official repository for the paper "High-Resolution Daytime Translation Without Domain Labels" (CVPR2020, Oral)
Complete-Life-Cycle-of-a-Data-Science-Project
Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx
Hierarchical Image Pyramid Transformer - CVPR 2022 (Oral)
Official implementation of the paper: MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (CVPR 2021)
Keras implementation of Representation Learning with Contrastive Predictive Coding
Python re-implementation of the (constrained) spectral clustering algorithms used in Google's speaker diarization papers.
This repository contains a reading list of papers on Time Series Segmentation. This repository is still being continuously improved.
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
Current state of supervised and unsupervised depth completion methods
PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Unsupervised video summarization with deep reinforcement learning (AAAI'18)
[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
The full collection of Jupyter Notebook labs from Andrew Ng's Machine Learning Specialization.
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.
Learning to Cluster. A deep clustering strategy.
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss
A distributed Spark/Scala implementation of the isolation forest and extended isolation forest algorithms for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
[CVPR 2017] Unsupervised deep learning using unlabelled videos on the web
The TensorFlow reference implementation of 'GEMSEC: Graph Embedding with Self Clustering' (ASONAM 2019).
pyge: Holiday Gift Exchange Picker
Coursera Specialization: Machine Learning and Data Analysis (Yandex & MIPT)
A stable algorithm for GAN training
Supplementary material for Hands-On Machine Learning with R, an applied book covering the fundamentals of machine learning with R.
t-Distributed Stochastic Neighbor Embedding (t-SNE) in Go
Vanilla GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to implementations of stable GAN variations (i.e. ACGan, InfoGAN) and other promising variations of GANs like conditional and Wasserstein.
A sparsity aware implementation of "Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection" (CIKM 2018).
Student–Teacher Anomaly Detection with Discriminative Latent Embeddings
An implementation of masked language modeling for Pytorch, made as concise and simple as possible
A Compositional Object-Based Approach to Learning Physical Dynamics
Implementation of ProteinBERT in Pytorch