Awesome GitHub RepositoriesGraph Coloring Algorithms

This project is a comprehensive library of reference implementations for fundamental data structures and algorithms, designed to support technical interview preparation and software engineering assessments. It provides a structured collection of computational techniques for solving complex problems involving arrays, strings, graphs, trees, and mathematical analysis. The library distinguishes itself by offering specialized implementations for advanced topics, including concurrent programming patterns and geometric algorithms. It features thread-safe primitives for managing shared state and tas

This project is a collection of reference implementations for algorithms, mathematics, cryptography, compression, and machine learning written in C#. It serves as an educational library providing standard implementations of sorting, searching, and graph theory algorithms. The repository covers a wide range of computational domains, including combinatorial optimization for constraint satisfaction and scheduling, as well as symmetric and classical cryptographic ciphers. It also provides reference code for lossless data compression techniques and fundamental machine learning primitives such as r

LogicStack-LeetCode is a curated repository of solved algorithm problems and data structure implementations, primarily drawn from the LeetCode platform. Its core identity is a structured collection of solutions designed to support technical interview preparation and competitive programming practice, with each solution accompanied by complexity analyses to help engineers understand performance trade-offs. The repository distinguishes itself through its breadth of coverage across fundamental algorithmic patterns and data structures. It includes implementations for array manipulation, string pro

Warp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera

Memgraph is an in-memory, distributed graph database designed for high-performance labeled property graph management. It utilizes a Cypher query engine for declarative data retrieval and manipulation, providing a scalable knowledge graph backend that integrates vector search and graph traversals. The system distinguishes itself as a real-time graph analytics platform, employing native C++ and CUDA implementations to execute complex network analysis and dynamic community detection on streaming data. It provides specialized support for AI integration, including GraphRAG capabilities, the constr

This project is a Go algorithm implementation library and a reference for data structures. It serves as a collection of solved coding interview problems and an algorithmic pattern collection, providing a reference of over 100 common challenges implemented in Go. The library focuses on specific problem-solving strategies, including sliding windows, two pointers, and dynamic programming. It provides coded examples of standard sorting, searching, and graph traversal techniques to facilitate the study of algorithmic patterns. The repository covers a broad range of capabilities, including array a

DifferentialEquations.jl este o bibliotecă numerică cuprinzătoare concepută pentru rezolvarea ecuațiilor diferențiale ordinare, stocastice, cu întârziere și algebrice. Funcționează ca o suită de solver-e de înaltă performanță care integrează machine learning științific, programare probabilistică și diferențiere automată într-un framework unificat. Prin valorificarea multiple dispatch-ului și a integrării simbolico-numerice, biblioteca oferă un mediu flexibil pentru modelare matematică complexă și simulare. Proiectul se distinge prin capacitatea sa de a face legătura între analiza numerică tradițională și tehnicile moderne de machine learning. Suportă antrenarea ecuațiilor diferențiale universale, permițând utilizatorilor să încorporeze rețele neuronale direct în simulatoare științifice pentru a învăța dinamici necunoscute, menținând în același timp constrângerile fizice. Mai mult, biblioteca oferă instrumente avansate de analiză a sensibilității și estimare a parametrilor, inclusiv metode adiacente și inferență Bayesiană, care permit calibrarea eficientă a modelelor și descoperirea automată a ecuațiilor guvernante din date. Platforma oferă capabilități extinse pentru calcul de înaltă performanță, inclusiv paralelism agnostic față de hardware care distribuie simulările pe CPU-uri, GPU-uri și clustere distribuite. Încorporează cuantificarea riguroasă a incertitudinii prin aritmetică de interval și propagare Monte Carlo, asigurând o estimare fiabilă a erorilor în experimentele numerice. În plus, sistemul dispune de rutine de optimizare sofisticate, cum ar fi detectarea rarității bazată pe grafuri și calculul produsului fără matrice, pentru a accelera performanța în sisteme la scară largă.