🗂️ Repository Structure Overview
This is a collaborative Obsidian vault for learning and documenting Machine Learning topics, projects, and resources. Below is a breakdown of the folder structure and its purpose.
📁 00-General
General notes about the project:
- Roadmap
- Repo structure (this file)
- Contribution guidelines
📁 01-Topics
The core learning material — structured by topic.
Each topic (e.g., 01-Math-Foundation) contains:
01-Overview.md— high-level summary02-Resources.md— links to articles, books, videos03-CodeExamples/— demos, notebooks04-Notes.md— detailed explanations05-Exercises.md— practice problems
📁 02-Papers
Summaries, notes, and insights from academic papers related to:
- Machine learning theory
- LLMs and NLP
- Novel techniques and models
📁 03-Projects
Hands-on applications of ML concepts:
- Mini-projects and capstones
- Experiments and implementations
- Group or individual work
📁 04-Meetings
Meeting notes and logs:
- Weekly planning
- Discussion summaries
- Task tracking and next steps
📁 05-Templates
Reusable content blocks and templates for:
- Meetings
- Topic overviews
- Paper reviews
- Project submissions
📁 06-CheatSheets
Quick reference guides:
- Syntax snippets
- ML workflows
- Math formulas
- Command-line cheats
📁 07-Resources
Useful external resources not tied to a specific topic.
📂 Datasets
Links and notes about datasets:
- Public datasets
- Usage examples
- Licensing info
📂 Tools
Curated ML tools:
- Libraries (e.g., scikit-learn, PyTorch)
- Platforms (e.g., HuggingFace, Weights & Biases)
- Utilities (e.g., label studio, Colab)