Slides/notes and Jupyter notebook demos for an introductory course of numerical analysis/scientific computing

A curated collection of machine learning, deep learning, and statistics resources. Includes Jupyter notebooks with notes, lab experiments, research explorations, and practical examples—perfect for learning, reference, and experimentation.

One-command Windows health check with AI-powered analysis. Scans thermals, memory, security overhead, startup bloat, and more.

Supply Chain Analytics - Notebooks for classroom.

Visual and interactive guide to optimization algorithms — from gradient descent to Adam, with Python notebooks and animations

Jupyter and Pluto notebooks for Operations Research Problems

Мaterials for Quantitative Methods Exercise Classes (taught in Bulgarian), including Linear Programming, Simplex algorithms, and graphical solutions: A collection of Jupyter notebooks and Python scripts

🔬 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.

Decode cellphone transmissions and compare performance to decoding methods currently in use in cellular networks.

Jupyter Notebook exploring Dynamic Economic Emissions Dispatch (DEED) using reinforcement learning with OpenAI Gym.

A collection of Jupyter (IJulia) notebooks demonstrating optimization techniques in Julia using JuMP. Forked from JuliaOpt.

A comprehensive collection of Jupyter notebooks demonstrating advanced mathematical concepts using SageMath. Covers calculus, linear algebra, number theory, cryptography, optimization, and computational mathematics with interactive visualizations.

Python implementations of reinforcement learning algorithms from Sutton & Barto's book "Reinforcement Learning: An Introduction". Includes solutions to book exercises.

This project solves the Maximum Clique Problem using continuous optimization algorithms like Projected Gradient Descent and Frank-Wolfe, implemented in a Jupyter Notebook.

A hands-on machine learning study repository containing Jupyter notebooks that implement and explain core ML algorithms, optimization methods, model evaluation techniques, and practical experiments.

A teaching collection of handwritten PDF and Julia/Pluto notebooks, mainly numerical mathematics, optimization, partial differenatial equations and related topics.

Interactive Quantum Portfolio Optimization using QAOA (Quantum Approximate Optimization Algorithm) to solve the Mean-Variance Portfolio problem with real market data. Built with Qiskit, featuring Jupyter notebook walkthrough, QUBO formulation, and intelligent share allocation based on Sharpe ratios.

Notebooks on using, post-processing, and calibrating a discrete element method digital twin of a GranuTools GranuDrum using ACCES.

Mathematical explorations and first-principles implementations of core machine learning methods.

A set of PyTorch/Jupyter notebooks exploring optimization algorithms and training strategies (e.g., SGD/Adam variants) with convergence and loss/metric visualizations.