Topics / #unsupervised-learning

Unsupervised-learning projects on GitHub

Machine learning methods that find patterns in unlabeled data.

As of October 19, 2026, the most-starred Unsupervised-learning projects on GitHub are TensorFlow-Examples (43.7K stars), ML-From-Scratch (33K stars) and 100-Days-Of-ML-Code (22.2K stars). GitHub.Locker tracks 10 Unsupervised-learning repositories with 189.3K stars combined. The fastest-growing on October 18 was dinov2, with +11 stars in a day.

10Projects

189.3KStars in total

+29Stars in a day

Browse in the catalog →github.com/topics/unsupervised-learning ↗

Hot right now

Most stars gained in a day · Oct 18

+11

dinov2

facebookresearch/dinov2 · ★ 13.4K

DINOv2, a self-supervised visual feature learning method from Meta AI's FAIR team, shipped as PyTorch code and pretrained models. Its features plug into classifiers as simple as linear layers across many vision tasks, with no fine-tuning required.

awesome-artificial-intelligence

owainlewis/awesome-artificial-intelligence · ★ 16.7K

An opinionated, actively maintained collection of AI learning resources — courses, books, papers, and guides — for software developers building generative AI and agentic systems. It has over 16,600 GitHub stars.

ML-From-Scratch

eriklindernoren/ML-From-Scratch · ★ 33K

A Python library of fundamental machine learning models and algorithms implemented from scratch in NumPy, aiming to cover everything from linear regression to deep learning.

pyod

yzhao062/pyod · ★ 10K

PyOD is a Python library for anomaly detection that bundles over 60 detection algorithms into one consistent API, covering tabular, time series, graph, text, image, and audio data.

The all-time top

10 projects · by stars

#1 · aymericdamien/TensorFlow-Examples
TensorFlow-Examples
A hands-on tutorial collection that teaches TensorFlow through runnable examples, from a hello world notebook up to deep neural networks and GANs.
43.7K
#2 · eriklindernoren/ML-From-Scratch
ML-From-Scratch
A Python library of fundamental machine learning models and algorithms implemented from scratch in NumPy, aiming to cover everything from linear regression to deep learning.
33K
#3 · MLEveryday/100-Days-Of-ML-Code
100-Days-Of-ML-Code
The Chinese edition of the 100 Days of ML Code challenge: a day-by-day machine learning learning log with infographics, notes and Python code, spanning supervised and unsupervised learning. It translates the original challenge by Avik-Jain and has gathered 22k+ stars.
22.2K

4
stanford-cs-229-machine-learning
20.3Kafshinea/stanford-cs-229-machine-learning
A collection of cheatsheets and refreshers that summarize all the key concepts taught in Stanford's CS 229 Machine Learning course, covering supervised learning, unsupervised learning, deep learning, and training tips.
5
awesome-artificial-intelligence
16.7Kowainlewis/awesome-artificial-intelligence · Python
An opinionated, actively maintained collection of AI learning resources — courses, books, papers, and guides — for software developers building generative AI and agentic systems. It has over 16,600 GitHub stars.
6
stylegan
14.4KNVlabs/stylegan · Python
NVIDIA's official TensorFlow implementation of StyleGAN, a generative adversarial network that synthesizes photorealistic images — most famously 1024x1024 faces of people who don't exist — with intuitive control over attributes like pose and identity.
7
transferlearning
14.4Kjindongwang/transferlearning · Python
A curated knowledge base covering transfer learning, domain adaptation, domain generalization and related subfields - papers, tutorials, code, datasets, scholars and surveys, maintained in both English and Chinese.
8
dinov2
13.4Kfacebookresearch/dinov2 · Jupyter Notebook
DINOv2, a self-supervised visual feature learning method from Meta AI's FAIR team, shipped as PyTorch code and pretrained models. Its features plug into classifiers as simple as linear layers across many vision tasks, with no fine-tuning required.
9
pyod
10Kyzhao062/pyod · Python
PyOD is a Python library for anomaly detection that bundles over 60 detection algorithms into one consistent API, covering tabular, time series, graph, text, image, and audio data.
10
Unsupervised-Log-Anomaly-Detection-Microservices
1Kchuansirrr/Unsupervised-Log-Anomaly-Detection-Microservices · Python
It is a research-grade, unsupervised anomaly detection framework for microservice logs that combines Transformer, LSTM, and statistical features with graph-based modeling and ensemble scoring to flag unusual log behavior without labeled data.