Machine Learning Notebooks

Optimization notebooks

A collection of various scientific notebooks ✨

Travelling Salesman Problem: playing with various optimisation algorithms

This repo features my Jupyter notebook implementations of robotics learning and optimization codes, inspired by https://rcfs.ch/.

Supply Chain Analytics - Notebooks for classroom.

A web notebook for exploring operations research problems

Notebook of PelajarHsinchu Top16% on BRI Data Hackaton 2021

Jupyter notebooks to describe the principles of the Tomography scheme

Report and notebook about Optimization for Machine Learning using Dynamic Sampling

Collection of example Jupyter Notebooks and R Markdown files for predictive analytics

Notebooks for Foundation of Machine Learning (PHYS 243) Fall 2021 course at UCR

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.

A from-scratch implementation of reverse automatic differentiation and gradient descent in C++, featuring Python bindings for Jupyter notebook integration.

Here I will save the notebooks for the Introduction to Optimization course projects, at the National University of Colombia 2022-1

Implementation of an binary classification optimization model solved from logit perspective in a jupyter notebook using python and Scipy.optimize library

📶 Logistic regression classifier for bit decoding in binary vectors using stochastic gradient descent (SGD). Features performance evaluation, probabilistic modeling, confusion matrix analysis, and classification error interpretation. Developed in Python with Jupyter Notebook.

Eli Goldratt his PQ-Problem with a solution for Mathematica.

🔬 SciPyMasterPro — A hands-on, modular project to master SciPy for statistics, optimization, linear algebra, curve fitting, and simulations. Includes 10+ Jupyter notebooks, an interactive Streamlit app, synthetic datasets, reusable utility functions, Dockerized setup, and cheatsheets for fast recall, portfolio building, and interview prep.