This project is a comprehensive collection of computer science implementations and an algorithm tutorial repository. It serves as a study guide and reference for competitive programming, providing executable code examples that demonstrate fundamental algorithmic problem solving and mathematical computation.
The library covers a wide range of specialized domains, including cryptography and security primitives, lossless data compression techniques, and computational geometry for spatial analysis. It also features implementations of machine learning models, linear algebra operations, and formal language parsing.
The collection includes extensive resources on graph algorithms, fundamental data structures, numerical methods, and logic-based algorithm simulations. It also provides utilities for measuring code execution time to analyze performance.
The implementations are provided as Jupyter Notebooks.