Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble

FAst Lookups of Cosine and Other Nearest Neighbors (based on fast locality-sensitive hashing)

Open source audio fingerprinting in .NET. An efficient algorithm for acoustic fingerprinting written purely in C#.

Selected Machine Learning algorithms for natural language processing and semantic analysis in Golang

Go to: https://github.com/alexklibisz/elastiknn

JavaScript port of TLSH (Trend Micro Locality Sensitive Hash)

TLSH lib in Golang

Scalable and Sustainable Deep Learning via Randomized Hashing

Dice.com repo to accompany the dice.com 'Vectors in Search' talk by Simon Hughes, from the Activate 2018 search conference, and the 'Searching with Vectors' talk from Haystack 2019 (US). Builds upon my conceptual search and semantic search work from 2015

An implementation of efficient LSH inspired by fruit fly brain

One-Shot Learning using Nearest-Neighbor Search (NNS) and Locality-Sensitive Hashing LSH

Locality Sensitive Hashing In R

Fast and precise comparison of genomes and metagenomes (in the order of terabytes) on a typical personal laptop

Software for exploration of gene expression data from single-cell RNA sequencing.

A tutorial on scalable retrieval of matrix factorization recommendations

TLSH (Trend Micro Locality Sensitive Hash) library for Ruby

Quickly estimate the similarity between many sets

BagMinHash - Minwise Hashing Algorithm for Weighted Sets

Minhash and maxhash library in Python, combining flexibility, expressivity, and performance.

Implementing various machine learning algorithm from scratch

Java port of TLSH (Trend Micro Locality Sensitive Hash)

Generate kmers/minimizers/hashes/MinHash signatures, including with multiple kmer sizes.

ProbMinHash – A Class of Locality-Sensitive Hash Algorithms for the (Probability) Jaccard Similarity

A native go implementation of spamsum

MinHash and LSH index written in Rust for Node.js

Built text and image clustering models using unsupervised machine learning algorithms such as nearest neighbors, k means, LDA , and used techniques such as expectation maximization, locality sensitive hashing, and gibbs sampling in Python

Website for "A survey of learning to hash for Computer Vision" https://learning2hash.github.io