This repository provides a curated list of the top 200 deep learning GitHub repositories, sorted by star count as of February 2020. It serves as a valuable resource for researchers, engineers, and students looking to identify influential and widely-used projects in the deep learning landscape. The list helps users quickly discover foundational frameworks, popular libraries, and key research implementations.

How It Works

The repository leverages the GitHub Search API to query for repositories related to "deep-learning" and common subfields like "CNN" and "RNN." Results are then ranked by the number of stars, a proxy for community interest and project popularity. The data is presented in a table format, including repository name, description, primary language, star count, and fork count, allowing for easy comparison and identification of trending projects.

Quick Start & Requirements

This repository is a curated list and does not require installation or execution. It serves as a reference guide.

Highlighted Details

• Lists foundational frameworks like TensorFlow, Keras, and PyTorch.
• Includes popular computer vision libraries such as OpenCV and Detectron.
• Features prominent NLP resources like spaCy and AllenNLP.
• Highlights educational materials and tutorials for learning deep learning concepts.

Maintenance & Community

The repository was last updated on February 2, 2020, indicating it is not actively maintained. Community engagement is reflected in the star counts of the listed projects, not within this specific repository.

Licensing & Compatibility

The licensing of the listed repositories varies. This repository itself does not impose any licensing restrictions on its content, as it is a compilation of publicly available information.

Limitations & Caveats

The data is static and reflects the state of GitHub as of February 2020. It does not include more recent or trending projects that have emerged since that date. The sorting is solely based on star count, which may not always correlate with project quality or relevance for specific use cases.