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Deep Learning and deep reinforcement learning research papers and some codes

Awesome Deep learning papers and other resources

Awesome

A list of recent papers regarding deep learning and deep reinforcement learning. They are sorted by time to see the recent papers first. I will renew the recent papers and add notes to these papers.

You should find the papers and software with star flag are more important or popular.

Table of Contents

  • Papers
  • Model Zoo
  • Pretrained Model
  • Courses
  • Books
  • Tutorials
  • Software
  • Applications
  • Awesome Projects
  • Corpus

Papers

  • 2021 year
    • computer vision
    • natural language process
    • multi model
  • 2020 year
    • deep learning
    • deep reinforcement learning
    • natural language process
    • computer vision
  • 2019 year
    • deep learning
    • deep reinforcement learning
    • natural language process
    • computer vision
  • 2018 year
    • deep learning
    • deep reinforcement learning
    • natural language process
    • computer vision
  • 2017 year
    • deep learning
    • deep reinforcement learning
    • natural language process
    • computer vision
  • 2016 year
    • deep learning
    • deep reinforcement learning
    • natural language process
    • computer vision
  • 2015 year
  • 2014 year
  • 2013 year
  • 2012 year
  • 2011 year
  • 2010 year
  • before 2010 year

Model Zoo

  • 2012 | AlexNet: ImageNet Classification with Deep Convolutional Neural Networks. pdf code
  • 2013 | RCNN: Rich feature hierarchies for accurate object detection and semantic segmentation. arxiv code
  • 2014 | CGNA: Conditional Generative Adversarial Nets. arxiv code
  • 2014 | DeepFaceVariant: Deep Learning Face Representation from Predicting 10,000 Classes. pdf code
  • 2014 | GAN: Generative Adversarial Networks. arxiv code
  • 2014 | GoogLeNet: Going Deeper with Convolutions. pdf code

More details in Model Zoo

Pre Trained Model

More details in Pretrained Model

Courses

More details in courses

Books

More details in books

Tutorials

More details in tutorials

Software

More details in software

Applications

More details in applications

Awesome Projects

More details in awesome projects

Corpus

More details in corpus

Contributors

Special thanks to everyone who contributed to this project.

  • raer6
  • isikdogan
  • outlace
  • divamgupta
  • Naman-Bhalla
  • ppuliu
  • benedekrozemberczki
  • roziunicorn
  • von-latinski

License

The details in License