Unsupervised Learning
Showing 36 of 38 projects
Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility and transparency.
Python implementations of popular machine learning algorithms from scratch with interactive Jupyter demos and mathematical explanations.
Comprehensive cheatsheets and refreshers covering all key concepts from Stanford's CS 229 Machine Learning course.
A curated list of must-use resources for AI engineering, including books, courses, papers, frameworks, and tools.
A curated collection of must-use resources for AI engineering, including books, courses, papers, frameworks, and tools.
A curated collection of papers, code, and resources for domain adaptation in machine learning.
A Python library providing extensions and utilities for data science and machine learning tasks.
A comprehensive Rust machine learning framework focused on preprocessing and classical algorithms, akin to scikit-learn.
A curated list of research papers, datasets, and resources for anomaly detection in time-series, video, and image data.
A Python library for outlier, adversarial, and drift detection in machine learning models, supporting tabular, text, image, and time series data.
An API-oriented Python framework for unsupervised learning on graphs, featuring node/graph embeddings and community detection.
An unsupervised learning framework for depth and ego-motion estimation from monocular videos using TensorFlow.
A general-purpose machine learning library for Rust, focusing on speed and ease of use with minimal dependencies.
A Python implementation of Restricted Boltzmann Machines for binary factor analysis and collaborative filtering.
A fast, ergonomic machine learning library for Rust with broad algorithm coverage and WASM-first defaults.
MatLab/Octave implementations of popular machine learning algorithms with detailed mathematical explanations and code examples.
TensorFlow implementation of unsupervised cross-domain image generation for transferring images between domains like SVHN to MNIST.
A Python machine learning package for incremental learning on streaming data with concept drift detection.
A Python library for Bayesian inference in Hidden Markov Models (HMMs) and Hidden semi-Markov Models (HSMMs) with nonparametric extensions.
A Python library implementing Self-Organizing Maps (SOM) with batch training, PCA initialization, and visualization tools.
A multilingual command-line sentence tokenizer written in Go, ported from NLTK's Punkt system.
An AI-powered captcha solver using SimGAN to generate synthetic training data without manual labeling.
A factor analysis framework for unsupervised integration of multi-omics datasets.
A Julia package providing comprehensive clustering algorithms and validation metrics for data analysis.
An unsupervised machine learning approach to learn vector representations of molecular substructures for cheminformatics.
A distributed Spark/Scala implementation of Isolation Forest and Extended Isolation Forest algorithms for scalable unsupervised outlier detection.
Tutorial materials for the 2012 IPAM Graduate Summer School on Deep Learning and Feature Learning using Theano and Torch.
A collection of neuroevolution experiments for reinforcement learning control problems using unsupervised learning feature extractors.
A collection of scripts for training random forests and sparse filtering models on Kaggle datasets.
A Ruby implementation of k-means clustering with k-means++ initialization, silhouette scoring, and multiple runs for optimal results.
A Ruby port of the NLTK Punkt algorithm for unsupervised, language-independent sentence boundary detection.
A Python library for unsupervised learning of hidden semi-Markov models with explicit durations.
A Torch package providing unsupervised learning modules and algorithms like autoencoders, PCA, and k-means.
A small machine learning library written in Clojure providing simple, concise implementations of ML algorithms.
An implementation of unsupervised image-to-image translation using Generative Adversarial Networks (GANs).
A PyTorch-based Python library for energy-based machine learning models, including Restricted Boltzmann Machines and Deep Belief Networks.
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