k-nearest-neighbors

🚀 efficient approximate nearest neighbor search algorithm collections library written in Rust 🦀 .

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

TOROS N2 - lightweight approximate Nearest Neighbor library which runs fast even with large datasets

Official code for "DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation" (TPAMI2022) and "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" (RecSys2020)

Learning to create Machine Learning Algorithms

The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

Java library for approximate nearest neighbors search using Hierarchical Navigable Small World graphs

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

Machine Learning Algorithms on NSL-KDD dataset

Interactive K-Nearest Neighbors machine learning algorithm in JavaScript.

Essential NLP & ML, short & fast pure Python code

GloVe word vector embedding experiments (similar to Word2Vec)

Segmentation and Identification of Vertebrae in CT Scans using CNN, k-means Clustering and k-NN

Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine

Today, using machine learning algorithms is as easy as "import knn from ..." but it doesn't really help if you want to learn how the algorithms work

A general purpose text classifier

Rcpp bindings for the approximate nearest neighbors library hnswlib

The original lightweight introduction to machine learning in Rubix ML using the famous Iris dataset and the K Nearest Neighbors classifier.

Time Series Forecasting of Walmart Sales Data using Deep Learning and Machine Learning