<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This repository serves as a comprehensive learning resource for Machine Learning, Deep Learning, and Computer Vision, targeting students and researchers. It consolidates course notes, code implementations, and personal insights, aiming to accelerate learning in these rapidly evolving fields.

How It Works

<2-4 sentences on core approach / design (key algorithms, models, data flow, or architectural choices) and why this approach is advantageous or novel.> The project is structured around distinct directories for key areas like Computer Vision (based on UMich EECS498/CS231N), Autonomous Driving (Autoware, BEV perception), Graphics (Games101), and foundational ML/DL concepts. It combines detailed Markdown notes, often requiring LaTeX rendering for formulas, with practical PyTorch code examples for tasks like image classification. The approach emphasizes augmenting existing open-source materials with personal understanding and future-focused content.

Quick Start & Requirements

• Primarily a collection of notes and code examples, not a deployable application.
• Users are advised to use Typora with LaTeX rendering for optimal viewing of Markdown notes.
• Specific dependencies like Python, PyTorch, CUDA, or GPU requirements are not explicitly detailed for a unified setup but are implied by the subject matter (DL/CV).
• Links to official course materials (EECS498, CS231N, Games101) are referenced.

Highlighted Details

• Extensive notes based on UMich EECS498 (an expansion of CS231N) and UCSB's Games101.
• Includes practical PyTorch code for image classification and projects related to Autonomous Driving (Autoware, BEV).
• Future plans include in-depth notes on NeRF, 3DGS, and Stanford's CS236 (Deep Generative Models).
• Content is actively updated, with significant additions planned for NeRF, 3DGS, and generative models.

Maintenance & Community

The repository shows active development with detailed future plans extending to 2026, including updates for new CS231N versions and advanced topics like Reinforcement Learning and GPT architecture. Collaboration is implied through shared notes and contributions. No specific community links (Discord, Slack) are provided.

Licensing & Compatibility

The repository's license is not specified in the README. This absence may imply a default restrictive copyright or an unstated permissive license, requiring clarification for commercial or derivative use.

Limitations & Caveats

Several sections, including Computer Graphics and Multi-Modal Learning, are noted as incomplete or not fully updated. The primary focus is on learning and note-taking rather than a production-ready framework.