cuVS - a library for vector search and clustering on the GPU

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

Raku package with algorithms for finding nearest neighbors for different sets of objects.

Nearest Neighbor Search with Neighborhood Graph and Tree for High-dimensional Data

Face detection and retrieval in image and video files.

Performance-portable geometric search library

PostgreSQL extension for spatial indexing on a sphere

An insurance company called "Sure Tomorrow" wants to solve some problems with the help of machine learning. As a Data Science we're Predict the amount of insurance claims that a new client might receive and Protect clients' personal data without breaking the model with masking

High performance nearest neighbor data structures (KDTree and BallTree) and algorithms for Julia.

Simple and efficient Python package for modeling d-dimensional Bravais lattices in solid state physics.

Benchmarks of approximate nearest neighbor libraries in Python

Assignment Submission for ITDAA4-12 module

The code repository for the paper: Peijie et al., Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering. IEEE TKDE, 2023.

Predicting market stock prices using various Machine Learning models. Data trained and tested up until 2017. Data found of Kaggle.

Python wrapper around C++ utility to compute local geometric features of a point cloud

Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].

Учебные материалы по курсам связанным с Машинным обучением, которые я читаю в УрФУ. Презентации, блокноты ipynb, ссылки

K-Nearest Neighbors Time Series Prediction with Invariances

Rcpp bindings for Annoy

This program will approximate the Traveling salesman problem using 3 three different algorithms (Nearest Neighbot, 2Opt, and 3Opt). There are 6 different combinations and each can be run individually or in suite as part of a benchmark test.