collection of numerical optimization methods
This repository contains a collection of mathematical optimization algorithms demonstrating underlying concepts in an interactive manner using Jupyter notebooks with Binder support:
The content list is given below and subject to further expansion:
Univarite Newton-Raphson Minimization
Bivariate Gradient Descent vs. Newton-Raphson
Bivariate Newton Root-finding
Conjugate Gradient
Gauss-Newton vs. Levenberg-Marquardt
Stochastic Gradient Descent (SGD)
Feature Engineering - Titanic
Expectation Maximization
Christopher Hahne
EPFL Course - Optimization for Machine Learning - CS-439
Jupyter notebooks for demo/projects, now that github supports them
Notebooks about Bayesian methods for machine learning
Computational Methods Course at UdeA. Forked and size reduced from:
Official content for Harvard CS109