This repository is a comprehensive, community-driven curated list of resources for PyTorch users, covering tutorials, papers, projects, and libraries across a vast spectrum of deep learning domains. It serves as a central hub for developers, researchers, and practitioners looking to explore, learn, and implement PyTorch-based solutions, from foundational concepts to cutting-edge applications like LLMs and geometric deep learning.

How It Works

The project functions as a living, evolving knowledge base, meticulously organized into hierarchical categories and subcategories. It aggregates links to official documentation, research papers, GitHub repositories, and practical code examples, providing a structured overview of the PyTorch ecosystem. The curation emphasizes breadth and depth, aiming to capture the most relevant and impactful resources for each topic.

Quick Start & Requirements

This is a curated list, not a runnable software package. No installation or execution is required.

Highlighted Details

• Extensive coverage of Large Language Models (LLMs), including tutorials, training methodologies, inference engines (e.g., VLLM), and specific models (e.g., Llama, Mistral).
• Detailed sections on Computer Vision tasks like Object Detection (YOLO variants, MMDetection), Segmentation, and Super-resolution.
• Broad coverage of Geometric Deep Learning, featuring libraries like PyTorch Geometric and various graph neural network architectures.
• Includes resources for advanced topics such as Continual Learning, Quantization, Explainability (SHAP, Captum), and Spiking Neural Networks.

Maintenance & Community

The project relies on community contributions via pull requests and issues. It links to the PyTorch Discussion Forum and StackOverflow tags for community interaction.

Licensing & Compatibility

The repository itself is a list of links and does not have a specific license. The linked resources will have their own licenses, which users must adhere to.

Limitations & Caveats

As a curated list, the quality and maintenance of individual linked resources are external to this project. The sheer volume of information may require significant effort to navigate and evaluate.