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study-resources

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A hand-curated, topic-organized library of the best ML education — 923 docs (391 arXiv papers, 474 Stanford/MIT/Karpathy/fast.ai lectures, 58 explainer articles), normalized to Markdown with full provenance. Open it in Obsidian or point your agent at it. A clean ML corpus for learning, RAG & fine-tuning.

  • Updated May 29, 2026
  • Python

A community-driven, language-agnostic space for mastering Data Structures & Algorithms. Collaborate on clean code, share comprehensive study notes or resources, and resolve doubts together via GitHub Discussions. Let's learn and grow together!

  • Updated Jun 16, 2026

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