Pseudo-Inverse, Gradient-Stochastic-Steepest Descent, Logistic Regression and LDA-QDA

Analytical and Numerical Approximation of functions

This repository consists of Lab Assignments for course Machine Learning.

Basic and advanced linear algebra and numerical problems, numerical algorithms, and techniques with multiple applications in the field of Computer Science.

MATLAB Numerical Optimization Methods

Fortran/Python linear algebra utilities

This repository will comprise primary optimization algorithms in Python language. Optimization is an extremely important part of machine learning.

Course assignments for CL 663: IIT Bombay

Implementation of a few optimization algorithms

Numerical optimization algorithms with examples in Python.

📉🏞️ Steepest Descent Algorithm for Water Molecules Energy Minimization 🔍🌊

The project involves a practical optimization problem that is modelled and solved using some mathematical optimization methods and software.

The implementation of advanced mathematical optimization methods

Contains a mathematical optimization project implemented in Python

Gradient descent approximation of orthogonal projection of a point on a convex periodic curve

Through this project we will try to understand working of Steepest-Descent and Gradient-Descent method and the differences between them.

Example Code for numerical optimization. Written in python.

Implementation of Steepest Descent Algorithm in TypeScript

Diffuse Optical Tomography (DOT) is an non-invasive optical imaging technique that measures the optical properties of physiological tissue using near infrared spectrum light. Optical properties are extracted from the measurement using reconstruction algorithm. This project uses the steepest descent method for reconstruction of optical data.

Demonstration of gradient descent methods