Spectral Tensor Train Parameterization of Deep Learning Layers

Fortran routines from "Solving Least Squares Problems" by C. Lawson and R. Hanson (1995)

Parallelization of QR decomposition with Householder transformation

matrix decomposition from scratch for matrix analysis and analysis course capstone of ucas

Different methods to QR factorization

Three algorithms (classical GS, modified GS, and Householder) for QR factorisation written in Julia. Included is a standard backsubstitution algorithm also for Julia.

Program solves a system of linear equations

Numerical Methods for Ill-Conditioned Matrices - A project with Hilbert Matrix

HoHoMa-MyPurchase is meant to be a helping assistant when it comes to shopping. A pre-calculated shoppinglist for the day, reminders and automatic analysis of demands and stock.

This repository contains implementations of common numerical algorithms using MATLAB.

Householder reflection QR. System of linear equations.

Supervised ML program which predicts with 65% accuracy whether there is a cat in a given picture.

Solving the Least Squares Problem via reduced QR factorization by Gram-Schmidt and by Householder triangularization.

This is a QR factorization of a complex matrix.

This repository contains the python implementation of various functions used in ML such as PCA and Linear Regression

PicturePrediction is based on machine-learning concepts, aiming to create automated predictions in relation with the nature of certain cats or non-containing cats pictures. Using a data set of given images, it may independently distinguish characteristics of a new picture with an accuracy up to 75%.