A Kogbetliantz-type SVD for general matrices.

A small portable C library with several utility functions.

Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skillsets. Suitable for statistician/econometrician, quantitative analysts, data scientists and etc. to quickly refresh the linear algebra with the assis…

Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)

Clone of the Bioconductor repository for the BiocSingular package.

PReconditioned Iterative MultiMethod Eigensolver for solving symmetric/Hermitian eigenvalue problems and singular value problems

This repository contains functions/codes related to different methods of machine learning for classification and clustering in python.

"This repository hosts an implementation of the Singular Value Decomposition (SVD) algorithm tailored for data mining tasks. SVD is utilized for efficient dimensionality reduction, aiding in the extraction of key patterns and features from large and complex datasets."

Using data driven numerical methods to perform system identification, model reduction, and design controllers.

Experimenting with Singular Value Decomposition

Various Small Projects on Various Subjects

A movie recommender. Collaborative and content based filtering hybrid model.

A multi-precision variant of the Hari-Zimmermann complex GSVD.

The Hari-Zimmermann complex generalized hyperbolic SVD and EVD.

Neural network compression with SVD

Using various forms of Singular Value Decomposition(SVD) for a recommendation and prediction system

A pure C# implementation of reasonably fast low-level routines for linear algebra operations

Natural language processing of job postings in order to gain insight into the data science job market.

Computational Linear Algebra course covering topics like iterative methods, matrix decompositions, and applications. It includes theoretical concepts, practical exercises, and code. Advanced methods like QR factorization, spectral theorem, and iterative solvers for linear systems.

practical linear algebra for data science (with python)