University assignments and sandbox projects connected to machine learning study.

A Deep Learning library that I'm writing for educational purposes.

Introduction to Python and Neural Network with Keras and PyTorch for beginners

use the data set and run the ipynb file

A next-gen solver for nonlinearly constrained nonconvex optimization. Modular and lightweight, it unifies iterative methods (SQP vs interior points) and globalization techniques (filter method vs merit function, line search vs trust region method) in a single framework. Competitive against IPOPT, filterSQP, SNOPT, MINOS and CONOPT

a lightweight deep-learning framework with ruby

This repository contains notes, slides, labs, assignments and projects for the Mathematics for Machine Learning and Data Science by DeepLearning.AI and Coursera.

Pytorch library to test optimizers by visualizing how they descend on a your images. You can draw your own custom loss landscape and see what different optimizers do.

Implementations of main Machine Learning Agorithms from scratch: Gaussian Mixture Model, Gradient Boosting, Adam, RMSProp, PCA, QR, Eigendecomposition, Decision Trees etc.

Parallel Reverse Mode Automatic Differentiation in C# for Custom Neural Network Development

Content: Classification, Sigmoid function, Decision Boundary, Cost function, Gradient descent, Overfitting, Regularisation

Content: Multivariate regression, Feature scaling, Polynomial regression, gradient descent, regression using sklearn

This GitHub repository is a treasure trove of hands-on learning experiences, featuring a diverse collection of Machine Learning projects and algorithms built entirely from scratch in Python.

Implementation of polynomial regression using principles from linear regression

Undergraduate thesis

Python Projects on varied topics for Data Analysis and Machine Learning

Delve into gradient descent visualization, exemplifying its concepts with an example and its role in linear regression modeling

scalar value gradient descent optimizer

Analytical and numerical techniques like gradient descent, genetic algorithm, ... to solve a convex unconstrained nonlinear optimization problem from scratchh without using any python library

Statistical optimization for AI and machine learning