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Deep Learning Papers Reading Roadmap

If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?"

Here is a reading roadmap of Deep Learning papers!

The roadmap is constructed in accordance with the following four guidelines:

- From outline to detail
- From old to state-of-the-art
- from generic to specific areas
- focus on state-of-the-art

You will find many papers that are quite new but really worth reading.

I would continue adding papers to this roadmap.


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 1 Deep Learning History and Basics

 1.0 Book

[0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. "Deep learning." An MIT Press book. (2015). [[html]](http://www.deeplearningbook.org/) (Deep Learning Bible, you can read this book while reading following papers.) :star::star::star::star::star:

 1.1 Survey

[1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444. [[pdf]](http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf) (Three Giants' Survey) :star::star::star::star::star:

 1.2 Deep Belief Network(DBN)(Milestone of Deep Learning Eve)

[2] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554. [[pdf]](http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf)(Deep Learning Eve) :star::star::star:

[3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. "Reducing the dimensionality of data with neural networks." Science 313.5786 (2006): 504-507. [[pdf]](http://www.cs.toronto.edu/~hinton/science.pdf) (Milestone, Show the promise of deep learning) :star::star::star:

 1.3 ImageNet Evolution（Deep Learning broke out from here）

[4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012. [[pdf]](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) (AlexNet, Deep Learning Breakthrough) :star::star::star::star::star:

[5] Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014). [[pdf]](https://arxiv.org/pdf/1409.1556.pdf) (VGGNet,Neural Networks become very deep!) :star::star::star:

[6] Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf) (GoogLeNet) :star::star::star:

[7] He, Kaiming, et al. "Deep residual learning for image recognition." arXiv preprint arXiv:1512.03385 (2015). [[pdf]](https://arxiv.org/pdf/1512.03385.pdf) (ResNet,Very very deep networks, CVPR best paper) :star::star::star::star::star:

 1.4 Speech Recognition Evolution

[8] Hinton, Geoffrey, et al. "

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