This repository is a curated list of deep learning and deep reinforcement learning research papers, software, courses, and datasets, primarily aimed at researchers and practitioners in the field. It provides a structured overview of key resources, enabling users to quickly identify influential papers, popular tools, and foundational learning materials.

How It Works

The repository organizes resources into categories such as Papers (sorted by year), Model Zoo, Pretrained Models, Courses, Books, Tutorials, Software, Applications, Awesome Projects, and Corpus. Papers are listed with links to PDFs and code where available, with starred items indicating higher popularity or importance. The software section highlights popular deep learning frameworks and libraries.

Quick Start & Requirements

• Installation: No direct installation is required as this is a curated list. Users access resources via provided links.
• Prerequisites: Access to the internet to view linked papers, code repositories, and course materials. Some linked software may have specific dependencies (e.g., Python, CUDA for deep learning frameworks).
• Resources: Links to official documentation, demos, and project pages are provided for most listed software and projects.

Highlighted Details

• Comprehensive coverage of deep learning papers from 2010 to the present, sorted chronologically.
• Extensive lists of popular deep learning software (e.g., PyTorch, TensorFlow, Keras, Caffe) and applications.
• Curated lists of courses, books, and tutorials for self-paced learning.
• A dedicated section for datasets relevant to NLP and other AI tasks.

Maintenance & Community

The project acknowledges contributions from various individuals. Further community engagement details (e.g., Discord, Slack) are not explicitly mentioned in the README.

Licensing & Compatibility

The repository itself does not appear to have a specific license listed, but it aggregates links to resources that may have their own licenses. Users should verify the licensing of individual linked papers, software, and datasets.

Limitations & Caveats

The quality and maintenance of linked resources are dependent on their original sources. The "starred" items are subjective indicators of popularity and may not always reflect the most cutting-edge or relevant advancements.