Locality Sensitive Hashing, fuzzy-hash， min-hash, simhash, aHash, pHash, dHash。基于 Hash值的图片相似度、文本相似度

Search your object with hash

distill large scale web page text

Recommendation System on cryptocurrency, using data collected from users' tweets + 10-Fold Cross Validation ( Based on the cryptocoins from each user's tweets, the program runs algorithms on the data, resulting in the recommendation of other cryptocoins for each user) ( readme in greek but soon to be translated in English )

An implementation of Locality sensitive hashing

A Robust Library in C# for Similarity Estimation

Dataset deduplication using the spark ML lib and Scala

In this repository you can find an implementation of LSH (Local | Sensitive Hashing) and Finesse algorithms, designed to find similar data based on their hashes

📈 kNN using LSH and Hypercube projection & Clustering using kMeans++ for n-dim polygonal curves and time series

Image Retrieval implementation using Deep Learning and Kernelized Locality-Sensitive Hashing

Image classification and unsupervised learning using latent space vectors produced by convolutional neural nets together with the original vectors space

This is a task using python to find number of similar songs within the provided songs set.

Vectors - Nearest neighbor search and Clustering using LSH, Hypercube (and Lloyd's only at the clustering) algorithms with L2 metric.

TTAK.KO-12.0276 LSH Recursive Hasher

Implementation tasks for multiple algorithms to process massive data. The algorithms are written in Python.

Implementation of various machine learning algorithms from scratch, including Linear Regression, K-Nearest Neighbors, Decision Trees, and K-Means clustering. Also done EDA on data, Implemented LSH, IVF, SLIC algorithms also with evaluation metrics

📈|Time Series - Nearest neighbor search and Clustering using LSH, Hypercube (and Lloyd's only at the clustering) algorithms with metrics: L2, Discrete and Continuous Fréchet.