Awesome GitHub RepositoriesScientific Workflow Decomposition

This project is an academic research framework and PhD mentorship roadmap designed to guide the transition from basic technical concepts to independent scientific research. It serves as a research workflow guide and project management system for identifying scientific problems, designing technical solutions, and executing experiments for academic publication. The system provides a structured methodology for translating long-term scientific objectives into actionable roadmaps, publications, and technical milestones. It includes a scientific writing guide and a set of presentation toolkits cont

CS-Xmind-Note is a collection of structured mind maps and conceptual diagrams serving as a comprehensive knowledge base for computer science fundamentals. It functions as an academic reference and study guide, organizing core subjects into a visual mapping of interdependent technical concepts. The project utilizes an XMind-compatible schema to model complex domains through hierarchical nodes and relational concept mapping. This approach allows for the visual representation of technical layers, linking hardware specifications to software abstractions. The knowledge base covers several primary

This project is a curated knowledge repository providing theoretical guides, practical challenge banks, and professional handbooks for technical interview preparation in data science and machine learning. It serves as a comprehensive study resource that combines theoretical knowledge with algorithmic practice. The repository features specialized study resources including a probability and statistics handbook, a machine learning reference for algorithms and neural network architectures, and a coding and SQL challenge bank designed to simulate recruitment assignments. It also includes a technic

This project is a machine learning study guide and technical knowledge base. It serves as a version-controlled repository of mathematical formulas and algorithmic explanations, providing instructional material and reference notes for the study of artificial intelligence. The content is structured as a markdown-based knowledge base that pairs theoretical mathematical explanations directly with code implementations. This approach demonstrates model mechanics in practice across several specialized domains, including deep learning research, probabilistic graphical modeling, and reinforcement lear

ML-foundations est un curriculum éducatif en machine learning et un guide d'étude en informatique. Il fournit un chemin d'apprentissage structuré axé sur les fondements mathématiques et les prérequis computationnels nécessaires pour étudier le machine learning. Le projet sert de cours de mathématiques Python, délivrant des notebooks interactifs et des exercices de codage pour enseigner l'algèbre linéaire, le calcul et les statistiques. Il traduit des formules mathématiques abstraites en code algorithmique concret pour aider les apprenants à comprendre les principes sous-jacents aux algorithmes de machine learning. Le curriculum couvre les prérequis en science des données et les concepts de programmation fondamentaux via un apprentissage interactif basé sur des notebooks. Il utilise une décomposition modulaire des sujets pour organiser les matières dans des répertoires isolés pour une étude indépendante.