A small portable C library with several utility functions.
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Updated
Sep 6, 2024 - C
A small portable C library with several utility functions.
This repository contains a Python implementation of Principal Component Analysis (PCA) for dimensionality reduction and variance analysis. PCA is a powerful statistical technique used to identify patterns in data by transforming it into a set of orthogonal (uncorrelated) components, ranked by the amount of variance they explain.
Assorted C++-ified LAPACK routines for reference.
A library providing modules with easy to use matrix operations for real(8) and complex(8) arrays
Lanczos program for large sparse Hermitian matrices (Fortran 95 with C99 interface)
A PyGame simulation of the motion over time for an interconnected mass-spring-system with n masses
ForEig - A Fortran library for eigenvalue and eigenvector calculations.
This MATLAB program is designed to calculate eigenvalues and eigenvectors of a square matrix provided by the user. Eigenvalues and eigenvectors are fundamental concepts in linear algebra and have various applications in mathematics, science, and engineering.
DCGeig is a solver for large, sparse generalized eigenvalue problems with real symmetric positive definite matrices. It computes eigenvalues and eigenvectors and can be used, e.g., for computing eigenfrequencies of finite element models.
Computational Linear Algebra - Python
Linear Algebra Step by Step by Kuldeep Singh. Solved step by step through LaTeX
Fortran 90/95 adaptation of a couple of EISPACK Fortran 77 routines
This repository contains all the quizzes and assignments required to complete all 3 courses of the Mathematics for Machine Learning Specialization on Coursera
Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
In this project, I've used College/University data to perform Principal Component Analysis and provide its implications to the business
Imperial College London »Mathematics for Machine Learning«. A sequence of 3 courses on the prerequisite mathematics for applications in data science and machine learning. (1) Linear Algebra (2) Multivariate Calculus and (3) Principal Component Analysis (completed Sept. 10th, 2018)
The software is an implementation of the enriched subspace iteration method for solving the generalized eigenvalue problems.
Three C++ projects assigned for the Numerical Methods for Electrical Engineering (EE 242) course in the Spring 2021 semester.
This program is implemented as a project for EE 242 course, and it implements Normalized Power Iteration with Deflation algorithmm to calculate most dominant eigenvalue, its eigenvector and the second most dominant eigenvalue.
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