Projects & Notebooks of Coursera's Self-Driving Cars Specialization.

Jupyter Notebooks demonstrating Optimization using Python with case studies

Operations research (RO) problem by Algerie Telecome Satellite (ATS) hosted by SOAI Algiers (HAIK)

Collection of Machine Learning notebooks

Experiments and notes on Machine Learning.

Collection of notebooks accompanying a research paper on evaluating GHG emissions from hydroelectric, multipurpose and irrigation reservoirs in Myanmar

My Management science, and Mathematical Engineering Optimizations python notebooks.

This repository contains four optimization algoritms using Python and Jupyter Notebook. Portuguese: Este repositório contém quatro algoritmos de otimização utilizando Python e Jupyter Notebook.

This repository provides practical implementations, examples, and insights into various optimization methods, making it easier to understand and apply these concepts.

This repository holds the Jupyter notebooks coded throughout the Optimization course at Los Andes University, 2024-1 Semester. Notebooks include topics related to Linear Programming, Convex Optimization, and Transportation/Graph Algorithms.

Notebook with several optimization algorithms used to find the minimum of the Rosenbrock function

Jupyter Notebook defining, visualizing, and executing a gradient descent algorithm for optimization of three-dimensional cost functions.

Jupyter notebooks, scripts, and results associated with the paper Visualization of Optimization Algorithms by Marco Morais (Morais, 2020).

Python data structures and algorithms. Machine learning Jupyter Notebook, Numpy, Keras, TensorFlow, Neural Network, etc. Application of Python in engineering and data science.

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.

This repository explores two optimization algorithms: the Traveling Salesman Problem (TSP) and Nearest Neighbor Search (NNS). It features Jupyter notebooks implementing brute-force solutions to both problems, utilizing Euclidean distance calculations and path visualizations. Ideal for learning about algorithm efficiency and optimization techniques.