FAST Change Point Detection in R

This toolkit is a curated collection of machine learning projects, resources, and utilities designed to assist both beginners and seasoned practitioners in their journey through the fascinating world of machine learning.

A next-gen solver for optimization with nonconvex objective and constraints. Reimplements filterSQP (SQP) and IPOPT (barrier/interior-point method) in a modern and generic way, and unlocks methods never seen before. Competitive against filterSQP, IPOPT, SNOPT, MINOS and CONOPT.

Neural Networks built from scratch

A gentle introduction to custom gradient propagation for ML application in which parameters of LTI systems have to be optimized. This example enables the integration of control theory with machine learning, for the development of Physical-Informed Neural Networks (PINNs)

This repository will give you an idea how machine learning algorithms do work under the hood.

a lightweight deep-learning framework with ruby

This Repository contains material to learn about machine learning algorithms concepts along with implementation. This also provides you the material to prepare yourself for interviews.

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.

This project will cover some of the basic Artificial Intelligence along the course using Python. Mainly will use Numpy to build everything. I write all the files in Python and it refers back to the school labs at Dalhousie University.

AutoSGM

This project lets you learn who to implement a linear regression to predict the price of a car based on its mileage. (based on the 42 school subject)

Implementation of geodesic optimization methods in Julia. These methods generalize convex optimization for non-Euclidean problems.

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.

Gorgonia is a library that helps facilitate machine learning in Go.

Coding ML models, Sampling Methods, Feature Selection algorithms from scratch

numerical optimization in pytorch

Lightweight Python package for automatic differentiation

A modular C++17 framework for automatic differentiation