svd-factorization
There are 69 repositories under svd-factorization topic.
gu0y1/picture2pixel
A Python library for converting images into FPGA-displayable pixel art.
KingJamesSong/DifferentiableSVD
A collection of differentiable SVD methods and ICCV21 "Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?"
mayukh18/reco
a simple yet versatile recommendation systems library in python
soypat/lap
linear algebra package. like gonum/mat, but small. lets say gonum-lite
daniel-aime/recommender-system-by-daniel-aime
Projet d'étude système de recommendation en utilisant filtrage collaboratif
gnsaddy/book-recommendation-system-webapp
Book Recommendation System Web App
zeryabmoussaoui/SVD-Golub-Kahan
Singular Value computation using Golub-Kahan method
khyatimahendru/EigenfacesWithSVD
Facial Recognition on 'Labelled Faces in the Wild Dataset' using the concept of Eigenfaces. I have used Singular Value Decomposition to obtain the eigenfaces used.
Ayoub-etoullali/SVD-Singular-Value-Decomposition
This project demonstrates the application of Singular Value Decomposition (SVD) for image compression using Python and NumPy.
d-elicio/Music-Recommender-System-from-scratch
Design and implementation from scratch of different models for a musical recommendation system
oekosheri/Recommender-movie
A movie recommender served with a Flask-restful app
singhsidhukuldeep/Recommendation-System
Comparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering
jfilter/sparse-svd-benchmark
Sparse Truncated SVD Benchmark (Python)
mzalaya/collectivemc
Implementation of Collective Matrix Completion by Mokhtar Z. Alaya and Olga Klopp https://arxiv.org/abs/1807.09010
NadavShwartz93/Matrix_Decomposition
Matrix Calculator which perform SVD-factorization, QR-decomposition and LU-decomposition.
dean-sh/Movie-Recommender-System
A movie recommender system based on MovieLens (20M) dataset and FunkSVD algorithm
Sid2697/3D
Pipeline for point cloud to 3D object creation.
acharles7/large-scale-analytics
This repository contains PCA-analysis, SVD, and tf-idf examples from large scale analytics
ivan-pi/vanhuffel
Partial Total Least Squares routines from Sabine Van Huffel
Lravi15/Movie-Recommender-System
Model Based Collaborative Filtering Recommender system
SoniSiddharth/SamplingMethods_in_DataScience
Speeding up clustering algorithms using Sampling techniques (Lightweight Coresets)
tk-yoshimura/Algebra
Linear algebra
tk-yoshimura/MultiPrecisionComplexAlgebra
MultiPrecision Complex Algebra Implements
tuansunday05/ArticleRecommenderSystem
Using hybrid recommender system with apriori algorithm, content-based and collaborative filtering method for predicting users interactions and then recommend them for users.
wgurecky/CORRLA_RS
Correlation, sampling, sensitivity, inference and random linear algebra routines in rust
chlin907/CFRecommenderSystem
Collaborative Filtering Recommender system
dlt3/Recommendation-System
Normalized SVD based recommendation model
FelixJMartin/SVD-pattern-recognition
SVD-Based Handwritten Digit Classification This project implements a classification algorithm for handwritten digits using Singular Value Decomposition (SVD). It was developed as part of a miniproject on pattern recognition and scientific computing.
giladodinak/mlinc
Machine Learning from scratch in C
nasreen-ahmed/ml-algorithms
Repository of Machine learning Algorithms Implemented from scratch
npcrites/california-school-analysis
graduation rate / test score prediction for California public high schools
Pressio/pressio-distributed-linalg
Auxiliary tools: distributed SVD, QR, sample mesh, etc
somjit101/Netflix-Movie-Recommendation
A case study of the Netflix Prize solution where, given anonymous data of users and the ratings given to movies, the objective to provide recommendations to users for movies which they would like, based on their past activity and taste.
vigneshyanamalamanda/Movie_Recommender_System
This project is an interactive Movie Recommendation System built using the MovieLens 1M dataset, focusing on collaborative filtering techniques. The goal is to help users discover personalized movie suggestions based on their preferences and ratings, leveraging machine learning algorithms like KNN and SVD.