Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep Learning and deep reinforcement learning research papers and some codes
| Date | Stars |
|---|---|
| 2026-07-24 | 3021 |
| 2026-07-25 | 3021 |
| 2026-07-28 | 3021 |
| 2026-07-30 | 3021 |
| 2026-07-31 | 3022 |
| 2026-08-01 | 3021 |
| 2026-08-06 | 3021 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Awesome Deep learning papers and other resources
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](#papers)
- [Model Zoo](#model-zoo)
- [Pretrained Model](#pre-trained-model)
- [Courses](#courses)
- [Books](#books)
- [Tutorials](#tutorials)
- [Software](#software)
- [Applications](#applications)
- [Awesome Projects](#awesome-projects)
- [Corpus](#corpus)
# Papers
- [2021 year](papers/2021/cv.md)
- [computer vision](papers/2021/cv.md)
- [natural language process](papers/2021/nlp.md)
- [multi model](papers/2021/mm.md)
- [2020 year](papers/2020/dl.md)
- [deep learning](papers/2020/dl.md)
- [deep reinforcement learning](papers/2020/rl.md)
- [natural language process](papers/2020/nlp.md)
- [computer vision](papers/2020/cv.md)
- [2019 year](papers/2019/dl.md)
- [deep learning](papers/2019/dl.md)
- [deep reinforcement learning](papers/2019/rl.md)
- [natural language process](papers/2019/nlp.md)
- [computer vision](papers/2019/cv.md)
- [2018 year](papers/2018/dl.md)
- [deep learning](papers/2018/dl.md)
- [deep reinforcement learning](papers/2018/rl.md)
- [natural language process](papers/2018/nlp.md)
- [computer vision](papers/2018/cv.md)
- [2017 year](papers/2017/dl.md)
- [deep learning](papers/2017/dl.md)
- [deep reinforcement learning](papers/2017/rl.md)
- [natural language process](papers/2017/nlp.md)
- [computer vision](papers/2017/cv.md)
- [2016 year](papers/2016/dl.md)
- [deep learning](papers/2016/dl.md)
- [deep reinforcement learning](papers/2016/rl.md)
- [natural language process](papers/2016/nlp.md)
- [computer vision](papers/2016/cv.md)
- [2015 year](papers/2015.md)
- [2014 year](papers/2014.md)
- [2013 year](papers/2013.md)
- [2012 year](papers/2012.md)
- [2011 year](papers/2011.md)
- [2010 year](papers/2010.md)
- [before 2010 year](papers/before-2010.md)
# Model Zoo
* 2012 | AlexNet: ImageNet Classification with Deep Convolutional Neural Networks. [`pdf`](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) [`code`](https://github.com/kratzert/finetune_alexnet_with_tensorflow)
* 2013 | RCNN: Rich feature hierarchies for accurate object detection and semantic segmentation. [`arxiv`](https://arxiv.org/abs/1311.2524) [`code`](https://github.com/rbgirshick/rcnn)
* 2014 | CGNA: Conditional Generative Adversarial Nets. [`arxiv`](https://arxiv.org/abs/1411.1784) [`code`](https://github.com/zhangqianhui/Conditional-Gans)
* 2014 | DeepFaceVariant: Deep Learning Face Representation from Predicting 10,000 Classes. [`pdf`](http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf) [`code`](https://github.com/joyhuang9473/deepid-implementation)
* 2014 | GAN: Generative Adversarial Networks. [`arxiv`](https://arxiv.org/abs/1406.2661) [`code`](https://github.com/goodfeli/adversarial)
* 2014 | GoogLeNet: Going Deeper with Convolutions. [`pdf`](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf) [`code`](https://github.com/google/inception)
More details in [Model Zoo](model_zoo.md)
# Pre Trained Model
* [Aligning the fastText vectors of 78 languages](https://github.com/Babylonpartners/fastText_multilingual)
* [Available pretrained word embeddings](https://github.com/vzhong/embeddings)
* [Inception-v3 of imagenet](http://download.tensorflow.org/models/image/imagenet/inception-v3-2016-03-01.tar.gz)
* [Caffe2 Model Repository](https://github.com/caffe2/models)
More details in [Pretrained Model](pre_trained.md)
# Courses
* [Berkeley] [CS294: Deep Reinforcement Learning](http://rll.berkeley.edu/deeprlcourse/Excerpt of 16,759 characters
Read on GitHub
koala · @tencent
2.6k
Stjepan Jureković · Manning Publication · Croatia
10
Benedek Rozemberczki · @google · United Kingdom
4
Guillaume Chevalier · Canada
3
Leo Isikdogan
2
2
@hpcaitech · Singapore
2
2
2
1
1
Divam Gupta · United States
1
Denis Shilov · White Circle · France
1
Naman Bhalla · @scaleracademy · India
1
Neil Conway · Canada
1
Brandon B · United States
1
Honglei Liu
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7c31332903535c5e, topic:deep-learning, topic:neural-network
matched fp:7c31332903535c5e, topic:nlp
matched fp:7c31332903535c5e, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning
matched fp:7c31332903535c5e, topic:corpus, readme:corpus