This repository offers a comprehensive collection of learning materials and code for machine learning, deep learning, computer vision, and natural language processing, targeting individuals seeking systematic AI education. It provides practical project implementations and detailed notes across various AI subfields, aiming to accelerate learning and research.

How It Works

The project is structured into distinct sections covering diverse AI topics, from foundational algorithms to advanced models like Transformers and LLMs. It includes implementations of popular architectures (e.g., YOLOv5, Swin Transformer, PointNet) and techniques (e.g., GANs, Graph Neural Networks), alongside practical guides on data preprocessing, model optimization (ONNX), and specific applications like Named Entity Recognition and fraud prediction.

Quick Start & Requirements

• Installation: Primarily through cloning the repository and installing dependencies via pip.
• Prerequisites: Python 3.x, PyTorch, TensorFlow, Scikit-learn, ONNX Runtime, and potentially libraries for specific projects (e.g., OpenCV, Hugging Face Transformers). Specific CUDA versions may be required for GPU acceleration.
• Resources: Setup time varies based on project complexity and required libraries. GPU recommended for deep learning model training/inference.
• Links: No explicit quick-start guide or demo links are provided in the README.

Highlighted Details

• Extensive coverage of LLMs with 50 dedicated notes.
• Includes implementations of various Graph Neural Network models (GAT, GIN, GraphSAGE, etc.).
• Features practical applications like YOLOv5 with quantization-aware training and Swin Transformer.
• Provides resources on model conversion and runtime optimization using ONNX.

Maintenance & Community

• The repository is maintained by jhlucc.
• Community interaction is encouraged via GitHub Issues and Pull Requests. No specific community channels (Discord/Slack) or roadmaps are mentioned.

Licensing & Compatibility

• The README does not explicitly state a license.
• Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The repository appears to be a personal learning collection rather than a production-ready library, with no explicit versioning, testing, or deployment guidance. The lack of a specified license and clear setup instructions may hinder adoption.