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…

Selected Machine Learning algorithms for natural language processing and semantic analysis in Golang

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.

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

Fast truncated singular value decompositions

Randomized Dimension Reduction Library

Julia code for the book Numerical Linear Algebra

Digital Image Watermarking Method Based on Hybrid DWT-HD-SVD Technique: Attacks, PSNR, SSIM, NC

Randomized Matrix Decompositions using R

Building Recommendation Model for the electronics products of Amazon

Official Code for "EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting (ICCV 2023)"

A SciPy implementation of "GraRep: Learning Graph Representations with Global Structural Information" (WWW 2015).

Implementing Content based and Collaborative filtering(with KNN, Matrix Factorization and Neural Networks) in Python

Autodiff is a numerical library for the Go programming language that supports automatic differentiation. It implements routines for linear algebra (vector/matrix operations), numerical optimization and statistics

This repository contains lecture notes and codes for the course "Computational Methods for Data Science"

Building recommender systems in Julia

Demonstration of low rank matrix approximations via singular value decomposition

An introduction to matrix factorization and PCA and SVD.