Jupyter notebooks implementing Deep Learning algorithms in Keras and Tensorflow

The P-Median Problem project uses metaheuristic optimization to solve the p-median location problem, with Jupyter notebooks implementing random sampling and local search algorithms to minimize service distances.

A set of Jupyter notebooks that investigate and compare the performance of several numerical optimization techniques, both unconstrained (univariate search, Powell's method and Gradient Descent (fixed step and optimal step)) and constrained (Exterior Penalty method).

This repository contains comprehensive solutions and analyses for four key homework assignments in the realm of Statistical and Machine Learning. The repository is structured to include both the original PDFs of the homework assignments and Jupyter notebooks that provide detailed code solutions.