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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 papers reading roadmap for anyone who are eager to learn this amazing tech!
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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. --------------------------------------- # 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. "**Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups**." IEEE Signal Processing Magazine 29.6 (2012): 82-97. [[pdf]](http://cs224d.stanford.edu/papers/maas_paper.pdf) **(Breakthrough in speech recognition)**:star::star::star::star: **[9]** Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. "**Speech recognition with deep recurrent neural networks**." 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013. [[pdf]](http://arxiv.org/pdf/1303.5778.pdf) **(RNN)**:star::star::star: **[10]** Graves, Alex, and Navdeep Jaitly. "**Towards End-To-End Speech Recognition with Recurrent Neural Networks**." ICML. Vol. 14. 2014. [[pdf]](http://www.jmlr.org/proceedings/papers/v32/graves14.pdf):star::star::star: **[11]** Sak, Haşim, et al. "**Fast and accurate recurrent neural network acousti
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Dikshant Sagar · University of California, Irvine · United States
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Shreyas Padhy · Isomorphic Labs · United Kingdom
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Beibin Li · Apodex
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2b2d931f043b308d, topic:deep-learning
matched fp:2b2d931f043b308d, name:roadmap, desc:roadmap