Implemented various spellcheck techniques like cosine similarity, jaccard similarity and levenshtein distance. Open to any further contributions.
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Updated
Jun 11, 2024 - Jupyter Notebook
Implemented various spellcheck techniques like cosine similarity, jaccard similarity and levenshtein distance. Open to any further contributions.
Samples on how to use Azure SQL database with Azure OpenAI
Raku package for the computation of various distance functions.
This program calculates the distances between coordinates using a possible of three formulas: Vincenty, Cosines, or Haversine.
Pick Me A Flick: A content filtering based Movie Recommendation Engine .
Using SVD and other methods to compute movie recommendations using fake and real user movie ratings.
Tika-Similarity uses the Tika-Python package (Python port of Apache Tika) to compute file similarity based on Metadata features.
This repository contains a web application that integrates with a music recommendation system, which leverages a dataset of 3,415 audio files, each lasting thirty seconds, utilising a Locality-Sensitive Hashing (LSH) implementation to determine rhythmic similarity, as part of an assignment for the Fundamental of Big Data Analytics (DS2004) course.
Implementation of DTW algorithm between audio and midi files, plots of results and saving the path as JSON. A Sakoe-chiba band is the current optimization.
Movie recommendation system based on popularity and also using KNN and Cosine similarity. 🎥🍿
Реализация система извлечения изображений по текстовому описанию и поиск похожих фотографий
A collection of diverse recommendation system projects, spanning collaborative filtering, content-based methods, and hybrid approaches.
This repo contains the movie recommender system which uses vectorization, cosine similarity distance methods to calculate the most similar content based on movie tags/info.
This intelligent movie recommend system works on an advance machine learning model which learns the taste of a perticular user by collecting relevant data of his/her recently watched movies.
Basic example for searching code semantically in github profiles. In python
Deep Neural Net For Finding Similar Images With Hyperparameter Optimization + AWS And Azure GPU Capabilities
Content-based recommendation engine using Python and Scikitlearn, using concepts of Cosine distance and Euclidean distance. Finally, by using IMDB 5000 movie dataset built a content-based recommendation engine using CountVectorize and Cosine similarity scores between movies.
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