This study aims to explore the algorithm for the comparison of 3D point sets as a method for evaluating the camera position and unknown parameter estimators obtained using it.
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Updated
Jun 3, 2018 - Jupyter Notebook
This study aims to explore the algorithm for the comparison of 3D point sets as a method for evaluating the camera position and unknown parameter estimators obtained using it.
Machine learning model to recognise human faces & food dishes
SVD is basically a matrix factorization technique, which decomposes any matrix into 3 generic and familiar matrices. It has some cool applications in Machine Learning and Image Processing.
SVD movie recommender based on the target user ratings.
The repository focuses on utilizing Singular Value Decomposition (SVD) to reduce noise in images, addressing the critical task of image denoising in image processing.
Introducción al Aprendizaje No Supervisado en Español
Building Recommendation Model for the videogames products of Amazon
In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix that generalizes the eigendecomposition of a square normal matrix to any MxN matrix via an extension of the polar decomposition.
Summer@ICERM 2020 - Random Projections
The Hari–Zimmermann generalized SVD for CUDA.
Classification and clustering in dataset of news articles.
Homework for the Numerical Methods course @ ACS, UPB 2018
CS 189 Project T
The vectorized (AVX-512) batched singular value decomposition algorithm for matrices of order two.
Compilation of the assignments of the course of COL726: Numerical Algorithms (Spring 2021) and their solutions
Collection of some classical Machine learning Algorithms.
Trying out the SVD rigid body motion solve back in November 2019.
Using Singular Value Decomposition (SVD) to compress an image.
Final Project for STA 160 with Dr. Fushing Hsieh
practical linear algebra for data science (with python)
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