Awesome-Deeplearning-Resources

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awesome-deeplearning-resources is a curated list of deep learning and deep reinforcement learning research papers. It helps researchers and practitioners quickly find recent and important research.

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Pricing

free

Free tier

Yes

API

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Skill level

Technical

About

What is awesome-deeplearning-resources?

awesome-deeplearning-resources offers a curated collection of research papers in deep learning and deep reinforcement learning. The papers are meticulously organized by their publication date, enabling users to efficiently discover the most current advancements in the field. The list also highlights important or popular papers and associated software through a starring system. This resource is designed to support researchers and practitioners by providing a streamlined way to stay informed about key developments and foundational works in deep learning.

Best used for

Staying current with the latest and most impactful research in deep learning and deep reinforcement learning.

Common actions

Find research papers

Stay updated on deep learning

Discover deep reinforcement learning

Identify important research

Explore academic resources

github copilot"AI Agents"face swappingopen-sourcelow-code/no-codecollaborationworkflowsautomated workflowdeepfake

Capabilities

Key features

Curated paper list
Sorted by publication date
Starred important papers
Includes software

Target Audience

ResearchersPractitionersAcademicsStudents

Integrations

Not yet documented

Pricing & Plans

free

Free

FAQs

How frequently is the list of papers updated?

The 'awesome-deeplearning-resources' list is maintained by contributors, so updates depend on community activity. While there isn't a fixed schedule, new and important papers are added periodically to keep the resource current.

Can I contribute new papers or suggest improvements to the list?

Yes, contributions are welcome! You can typically suggest new papers, point out errors, or propose improvements by opening an issue or submitting a pull request on the project's GitHub repository, following their contribution guidelines.

How are papers selected and 'starred' for importance?

Papers are generally selected based on their relevance, impact, and novelty within deep learning and deep reinforcement learning. The 'starred' designation usually indicates foundational, highly cited, or particularly influential works, often determined by the maintainers and community consensus.

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Coding & Development › Open Source & Models

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