Deep Learning Study Roadmap
===

This roadmap is inspired by
- [Flood Sung](https://github.com/songrotek)'s "[Deep Learning Papers Reading Roadmap](https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md)", and
- [Simon Brugman](https://github.com/sbrugman)'s "[Deep Learning Papers](https://github.com/sbrugman/deep-learning-papers)" and
- [Terry](https://github.com/terryum)'s "[Awesome Deep Learning Papers](https://github.com/terryum/awesome-deep-learning-papers)" and
- [2017 類神經網路 效能議題 重要論文整理](https://hackmd.io/s/r1e6MOL3W)

Table of Content
===
[TOC]

Journal / Conferences / Contest
===
### Conferences
- ICANN: International Conference on Artificial Neural Networks ([2017](http://www.icann2017.org/))
- [ENNS](https://e-nns.org/): European Neural Network Societ
- CVPR: Computer Vision and Pattern Recognition ([2017](http://cvpr2017.thecvf.com/))
- ECCV: European Conference on Computer Vision ([2016](http://www.eccv2016.org/))
- ICML: International Conference on Machine Learning ([2017](https://2017.icml.cc/))
- [NIPS](https://nips.cc/): Neural Information Processing Systems

### Journal
- [JMLR](http://www.jmlr.org/): Journal of Machine Learning Research

### Contest
- [ILSVRC](http://www.image-net.org/challenges/LSVRC/): ImageNet Large Scale Visual Recognition Challenge

DL Basic Study Materials
===
### Books
- [Deep Learning](http://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016. An MIT Press book.
- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by Michael Nielsen. An online book.
- [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff) by Francois Chollet (Author of Keras)

### Tutorials
- [Unsupervised Feature Learning and Deep Learning Tutorial](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial) by Andrew Ng (U. Stanford)
- [LISA Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) by the LISA Lab directed by Yoshua Bengio, 2015 (U. Montréal)
- [Deep Learning Tutorial](http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf) by Yann LeCun (NYU, Facebook) and Marc’Aurelio Ranzato (Facebook). ICML 2013 tutorial.
- [Tutorial on Deep Learning for Vision](https://sites.google.com/site/deeplearningcvpr2014/) from CVPR ‘14
- [Deep Learning for Objects and Scenes](http://deeplearning.csail.mit.edu/) from CVPR '17 :star2:
- [How To Become A Machine Learning Engineer: Learning Path](https://medium.com/machine-learning-world/learning-path-for-machine-learning-engineer-a7d5dc9de4a4) by [Andrey Nikishaev](https://medium.com/@a.nikishaev) :+1:

### Online Courses
- [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks?authMode=login#) by Geoffrey Hinton

Deep Learning Gurus Talk About AI/ML/DL
===
1. [Talk] [Yoshua Bengio on Intelligent Machines (17-02-2016)](http://www.themindoftheuniverse.org/play?id=Yoshua_Bengio)
2. [AMA] [Geoffrey Hinton](https://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/), [Yann LeCun](https://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_yann_lecun/), [Yoshua Bengio](https://www.reddit.com/r/MachineLearning/comments/1ysry1/ama_yoshua_bengio/), [Michael I Jordan](https://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/), [Ian Goodfellow & Alexey Kurakin](https://www.quora.com/session/Adversarial-Machine-Learning/1), [Andrew Ng & Adam Coates](https://www.reddit.com/r/MachineLearning/comments/32ihpe/ama_andrew_ng_and_adam_coates/), [Jürgen Schmidhuber](https://www.reddit.com/r/MachineLearning/comments/2xcyrl/i_am_j%C3%BCrgen_schmidhuber_ama/)
3. [Video] [Heroes of Deep Learning, Interviews](https://www.youtube.com/playlist?list=PLfsVAYSMwsksjfpy8P2t_I52mugGeA5gR) (Andrew Ng interviews a lot of Deep Learning gurus)
4. [Video] [How the brain works, according to Geoff Hinton](https://www.youtube.com/watch?v=mlXzufEk-2E) (Joke)

Reviews
===
1. [Yann LeCun](http://yann.lecun.com/), [Yoshua Bengio](http://www.iro.umontreal.ca/~bengioy/yoshua_en/), and [Geoffrey Hinton](http://www.cs.toronto.edu/~hinton/). **Deep learning**. *Nature 521.7553 (2015): 436-444*, 2015. [[paper](http://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf)] :star2::star2::star2:
2. [Y Bengio](http://www.iro.umontreal.ca/~bengioy/yoshua_en/), A Courville, and P Vincent. **Representation Learning: A Review and New Perspectives**. Proc. *IEEE transactions on pattern analysis and machine intelligence 35 (8), 1798-1828*, 2013. [[paper](https://arxiv.org/pdf/1206.5538.pdf)]
3. [Yoshua Bengio](http://www.iro.umontreal.ca/~bengioy/yoshua_en/). **Learning Deep Architectures for AI**. Proc. of the *IEEE transactions on pattern analysis and machine intelligence, 35:1798–1828*, 2013. [[paper](https://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf)]
4. [Jürgen Schmidhuber](http://people.idsia.ch/~juergen/), et al. **Deep learning in neural networks: An overview**. In *Neural Networks Volume 61, Pages 85–117*, 2015. [[article](http://ac.els-cdn.com/S0893608014002135/1-s2.0-S0893608014002135-main.pdf?_tid=a58b70f4-4be4-11e7-9e64-00000aab0f26&acdnat=1496883286_a710d8c31d038a6fc4c07d1078d652af)]
5. [Yann LeCun](http://yann.lecun.com/), Leon Bottou, [Yoshua Bengio](http://www.iro.umontreal.ca/~bengioy/yoshua_en/), and Pattrck Haffner. **Gradient-Based Learning Applied to Document Recognition**. Proceedings of the IEEE, 86(11):2278-2324, November 1998. [[paper](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf)] **(This overview paper on the principles of end-to-end training of modular systems such as deep neural networks using gradient-based optimization showed how neural networks (and in particular convolutional nets) can be combined with search or inference mechanisms to model complex outputs that are interdependent, such as sequences of characters associated with the content of a document.)**

Future
===
1. Artificial Intelligence and life in 2030, 2016. [[paper](https://ai100.stanford.edu/sites/default/files/ai_100_report_0831fnl.pdf)]
2. A Berkeley View of Systems Challenges for AI, 2017. [[paper](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-159.pdf)]
3. [AI Index](http://aiindex.org/). [[2017 report](http://cdn.aiindex.org/2017-report.pdf)]

Network Models
===

## Deep Belief Network (DBN) (Milestone of Deep Learning Eve)
1. Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. **A fast learning algorithm for deep belief nets**. Neural computation 18.7 (2006): 1527-1554. [[paper](http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf)] **(Deep Learning Eve)**
2. Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. **Reducing the dimensionality of data with neural networks**. Science 313.5786 (2006): 504-507. [[paper](http://www.cs.toronto.edu/~hinton/science.pdf)] **(Milestone, Show the promise of deep learning)**

## ImageNet Evolution (Deep Learning broke out from here)
1. [Krizhevsky, Alex](https://www.cs.toronto.edu/~kriz/), Ilya Sutskever, and [Geoffrey E. Hinton](http://www.cs.toronto.edu/~hinton/). **Imagenet classification with deep convolutional neural networks**. Advances in neural information processing systems. 2012. [[paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)] **(AlexNet, Deep Learning Breakthrough, ILSVRC 2012 winner)**
2. [Matthew D Zeiler](http://www.matthewzeiler.com/), [Rob Fergus](http://cs.nyu.edu/~fergus/pmwiki/pmwiki.php). **Visualizing and Understanding Convolutional Networks.** *arXiv preprint arXiv:1311.2901*, 2013, *European Conference on Computer Vision*, 2014. [[paper](https://arxiv.org/abs/1311.2901)] **(ZFNet, ILSVRC 2013 winner)**
3. Simonyan, Karen, and Andrew Zisserman. **Very deep convolutional networks for large-scale image recognition**. arXiv preprint arXiv:1409.1556 (2014). [[paper](https://arxiv.org/pdf/1409.1556.pdf)] **(VGGNet,Neural Networks become very deep!)**
4. Szegedy, Christian, et al. **Going deeper with convolutions**. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [[paper](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf)] **(GoogLeNet, ILSVRC 2014 winner)**
5. [Kaiming He](http://kaiminghe.com/), Xiangyu Zhang, Shaoqing Ren, Jian Sun. **Deep Residual Learning for Image Recognition**. *arXiv preprint arXiv:1512.03385*, 2015. Proceedings of the *IEEE Conference on Computer Vision and Pattern Recognition*, 2016. [[paper](https://arxiv.org/pdf/1512.03385.pdf), [github](https://github.com/KaimingHe/deep-residual-networks), [talk](https://www.youtube.com/watch?v=1PGLj-uKT1w&feature=youtu.be)] **(Microsoft ResNet, ILSVRC 2015 winner)**

## Convolutional Neural Networks
1. [LeCun,Y.](http://yann.lecun.com/) et al. **Handwritten digit recognition with a back-propagation network**. In Proc. *Advances in Neural Information Processing Systems 396–404*, 1990. [[paper](http://yann.lecun.com/exdb/publis/pdf/lecun-90c.pdf)] **(This is the first paper on convolutional networks trained by backpropagation for the task of classifying low-resolution images of handwritten digits.)** :star2::star2:
2. Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu. **Spatial Transformer Networks**. In *Advances in Neural Information Processing Systems 28 (NIPS 2015)*. [[paper](https://arxiv.org/pdf/1506.02025.pdf), [demo](https://drive.google.com/file/d/0B1nQa_sA3W2iN3RQLXVFRkNXN0k/view)] (**This paper showed how to make your model invariant to images with different scales and rotations.**)

## Feedforward Network Design
### Activation Function
1. Vinod Nair, and [Geoffrey E. Hinton](http://www.cs.toronto.edu/~hinton/). **Rectified Linear Units Improve Restricted Boltzmann Machines**. In *International Conference on Machine Learning (ICML)*, 2010. [[paper](http://www.cs.toronto.edu/~hinton/absps/reluICML.pdf)] **(ReLU layer)**
2. Glorot, X., Bordes, A. & Bengio. Y. **Deep sparse rectifier neural networks**. In Proc. *14th International Conference on Artificial Intelligence and Statistics 315–323*, 2011. [[paper](http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf)] **(This paper showed that supervised training of very deep neural networks is much faster if the hidden layers are composed of ReLU.)**
3. [Ian J. Goodfellow](http://www.iangoodfellow.com/), David Warde-Farley, Mehdi Mirza, Aaron Courville, [Yoshua Bengio](http://www.iro.umontreal.ca/~bengioy/yoshua_en/). **Maxout Networks**. *arXiv preprint arXiv:1302.4389*, 2013. [[paper](https://arxiv.org/pdf/1302.4389.pdf)]

### Dropout
1. Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov. **Improving neural networks by preventing co-adaptation of feature detectors.** *arXiv preprint arXiv:1207.0580*, 2012. [[paper](https://arxiv.org/pdf/1207.0580.pdf)] (**Dropout**) :star:
2. [Nitish Srivastava](http://www.cs.toronto.edu/~nitish/), Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov. **Dropout: A Simple Way to Prevent Neural Networks from Overfitting**. In *Journal of Machine Learning Research*, June 2014. [[paper](http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf), [talk](https://www.youtube.com/watch?v=DleXA5ADG78)] :star2:

### Pooling
1. Dominik Scherer, Andreas Müller, Sven Behnke. **Evaluation of pooling operations in convolutional architectures for object recognition**. In *Artificial Neural Networks–ICANN*, 2010. [[paper](http://ais.uni-bonn.de/papers/icann2010_maxpool.pdf)]
2. [Y-Lan Boureau](http://www.notebleue.org/), Jean Ponce, Yann LeCun. **A theoretical analysis of feature pooling in visual recognition**. In *International Conference on Machine Learning*, 2010. [[paper](http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_BoureauPL10.pdf)] **(pooling layers analysis)**
3. [Y-Lan Boureau](http://www.notebleue.org/), Nicolas Le Roux, Francis Bach, Jean Ponce, Yann LeCun. **Ask the locals: multi-way local pooling for image recognition**. Proc. *International Conference on Computer Vision (ICCV'11)*, 2011. [[paper](http://cs.nyu.edu/~ylan/files/publi/boureau-iccv-11.pdf)]
4. MD Zeiler, R Fergus. **Stochastic Pooling for Regularization of Deep Convolutional Neural Networks**, *arXiv preprint arXiv:1301.3557*, 2013. [[paper](https://arxiv.org/pdf/1301.3557v1.pdf)]

### Others
1. Min Lin, Qiang Chen, Shuicheng Yan. **Network In Network**. *arXiv preprint arXiv:1312.4400*, 2013. [[paper](https://arxiv.org/pdf/1312.4400.pdf), [model](https://gist.github.com/mavenlin/e56253735ef32c3c296d)]

## Back Propagation
1. [Article] [Who invented backpropagation?](http://people.idsia.ch/~juergen/who-invented-backpropagation.html) by Jürgen Schmidhuber, 2014 (updated 2015)
2. David C. Plaut, Steven J. Nowlan, Geoffrey E. Hinton. **Experiments on Learning by Back Propagation**. 1986. [[paper](http://www.cs.toronto.edu/~fritz/absps/bptr.pdf)]
3. Rumelhart, D.E., Hinton, G.E. & Williams, R.J. **Learning Representations by Back-Propagating Errors**. *Nature 323, 533-536*, 1986. [[paper](http://www.cs.toronto.edu/~hinton/absps/naturebp.pdf)] **(This is one of the first papers that discovered backpropagation.)**

## Capsule Neural Networks
1. [Article] [神經網絡之父 Geoff Hinton 推翻畢生心血「反向傳播演算法」：打掉重來，AI 才有未來！](https://buzzorange.com/techorange/2017/09/22/geoffrey-hinton-fight-back-propagation/) [[機器之心原編輯](https://www.jiqizhixin.com/articles/2017-09-21-11), [原文](https://medium.com/intuitionmachine/the-deeply-suspicious-nature-of-backpropagation-9bed5e2b085e)]
2. [Article] [浅析 Hinton 最近提出的 Capsule 计划](https://zhuanlan.zhihu.com/p/29435406)
- [Video] What is wrong with convolutional neural nets? [2014](https://www.youtube.com/watch?v=rTawFwUvnLE&t=2311s) (talk given at MIT), [2017](https://www.youtube.com/watch?v=Mqt8fs6ZbHk) by Geoffrey Hinton
3. Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. **Dynamic Routing Between Capsules**. *arXiv preprint arXiv:1710.09829*, 2017. [[paper](https://arxiv.org/pdf/1710.09829v1.pdf), [導讀](https://zhuanlan.zhihu.com/p/30521353), [Github-Tensorflow](https://github.com/naturomics/CapsNet-Tensorflow), [Github-Keras](https://github.com/XifengGuo/CapsNet-Keras)]
- [Review] [一起讀 DYNAMIC ROUTING BETWEEN CAPSULES](https://data-sci.info/2017/11/06/%E4%B8%80%E8%B5%B7%E8%AE%80-dynamic-routing-capsules/) by "LEARNING BY HACKING"
- [Impl.] [先读懂CapsNet架构然后用TensorFlow实现，这应该是最详细的教程了](http://www.sohu.com/a/202438769_465975) [[繁中版](http://bangqu.com/oWkWNH.html)]

## LSTM Networks
1. Hochreiter, S. & Schmidhuber, J. **Long short-term memory**. *Neural Comput. 9, 1735–1780*, 1997. [[paper](http://www.bioinf.jku.at/publications/older/2604.pdf)] **(This paper introduced LSTM recurrent networks, which have become a crucial ingredient in recent advances with recurrent networks because they are good at learning long-range dependencies.)**

## Generative Adversarial Networks
1. Ian J. Goodfellow, et al. **Generative Adversarial Networks**. *arXiv preprint arXiv:1406.2661*, 2014. [[paper](https://arxiv.org/pdf/1406.2661v1.pdf)]
2. [Article] 看穿机器学习（W-GAN模型）的黑箱 (Part [1](https://mp.weixin.qq.com/s?__biz=MzA3NTM4MzY1Mg==&mid=2650813024&idx=1&sn=31e326bd79ed24f5f47b35091385b9ab&chksm=8485c46bb3f24d7d36d1a93b48d9f4d0335262b1152de0bd0f2f1d09527e4acb2ae3d4730913&scene=21#wechat_redirect), [2](https://mp.weixin.qq.com/s?__biz=MzA3NTM4MzY1Mg==&mid=2650813028&idx=1&sn=b971c2f1389179951eb5a67b84f1bb49&chksm=8485c46fb3f24d790a7f7c15e50b29eca7b7c080efdb821e7bff1f6b9b5d6fd5afa6bd4dcd36&scene=21#wechat_redirect), [3](https://mp.weixin.qq.com/s?__biz=MzA3NTM4MzY1Mg==&mid=2650813038&idx=1&sn=1549a6b27cbe2820e72c0f28be9b32c3&chksm=8485c465b3f24d737895de681b0dccdcbcd191a991bc405db5115b1b8c3a84fbc1c5d3ff70ba&scene=21#wechat_redirect))
3. Na Lei, Kehua Su, Li Cui, Shing-Tung Yau, David Xianfeng Gu. **A Geometric View of Optimal Transportation and Generative Model**. *arXiv preprint arXiv:1710.05488*, 2017. [[paper](https://arxiv.org/pdf/1710.05488v1.pdf), [導讀](https://mp.weixin.qq.com/s/7O0AKIUVYK7HRyvdRbUVkg)]
- [Article] [丘成桐演講全文：工程上取得很大發展，但理論基礎仍非常薄弱，人工智能需要一個可被證明的理論作爲基礎 | CNCC 2017](https://www.leiphone.com/news/201710/JXViV3L0nQuP1dTu.html)

Optimization
===
### Learning Algorithm
1. [Dauphin, Y](http://www.dauphin.io/). et al. **Identifying and attacking the saddle point problem in high-dimensional non-convex optimization**. In Proc. *Advances in Neural Information Processing Systems 27 2933–2941*, 2014. [[paper](https://arxiv.org/pdf/1406.2572.pdf)]
2. Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B. & LeCun, Y. **The loss surface of multilayer networks**. In Proc. *Conference on AI and Statistics*, 2014. [[paper](https://arxiv.org/pdf/1412.0233.pdf)]
3. Review: [An Overview of Gradient Descent Optimization Algorithms](http://sebastianruder.com/optimizing-gradient-descent/) [[paper](https://arxiv.org/pdf/1609.04747.pdf)].

### Model (Network) Compression
1. Song Han, Huizi Mao, William J Dally. **Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding**. In Proc. *ICLR*, 2016. [[paper (ICLR16 best paper award)](https://arxiv.org/pdf/1510.00149v5.pdf)]

Training
===
### Efficient Training
1. Priya Goyal, et al. **Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour**. *arXiv preprint arXiv:1706.02677*, 2017. [[paper](https://arxiv.org/pdf/1706.02677.pdf)]
2. [Blog] [Google Brain Residency](http://tinyclouds.org/residency/) by [Ryan Dahl](http://tinyclouds.org/) (the author of Nodejs), a summary of his journey in [Googl Brain Residency Program](https://research.google.com/teams/brain/residency/).
3. Yang You, et al. **100-epoch ImageNet Training with AlexNet in 24 Minutes**. *arXiv preprint arXiv:1709.05011*, 2017. [[paper](https://arxiv.org/pdf/1709.05011v3.pdf)]
4. Vivienne Sze, et al. **Efficient Processing of Deep Neural Networks: A Tutorial and Survey**. *arXiv preprint arXiv:1703.09039*, 2017. [[paper](https://arxiv.org/pdf/1703.09039v2.pdf), [導讀](https://www.jiqizhixin.com/articles/2017-03-30-4)] :star2:
- [Tutorial on Hardware Architectures for Deep Neural Networks](http://eyeriss.mit.edu/tutorial.html)
5. [GoogleBrain] Samuel L. Smith, Pieter-Jan Kindermans, Quoc V. Le. **Don't Decay the Learning Rate, Increase the Batch Size**. *arXiv preprint arXiv:1711.00489*, 2017. [[paper](https://arxiv.org/pdf/1711.00489v1.pdf)]

### Debugging
1. [Article] [My Neural Network isn't working! What should I do?](http://theorangeduck.com/page/neural-network-not-working?utm_campaign=Revue+newsletter&utm_medium=Newsletter&utm_source=The+Wild+Week+in+AI)

Transfer Learning
===
1. Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson. **How transferable are features in deep neural networks?**. Proc. *NIST*, 2014. [[paper](https://arxiv.org/pdf/1411.1792.pdf)]
2. Ali Sharif Razavian Hossein Azizpour Josephine Sullivan Stefan Carlsson. **CNN Features off-the-shelf: an Astounding Baseline for Recognition**. Proc. *CVPR*, 2014. [[paper](https://arxiv.org/pdf/1403.6382.pdf)]
3. Jeff Donahue, Yangqing Jia, et al. **DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition**. Proceedings of Machine Learning Research, 2014. [[paper](http://proceedings.mlr.press/v32/donahue14.pdf)]

Frameworks
===
1. Kuo Zhang, Salem Alqahtani, Murat Demirbas. **A Comparison of Distributed Machine Learning Platforms**. *In Proceedings of ICCCN*, 2017. [[paper](https://www.cse.buffalo.edu/~demirbas/publications/DistMLplat.pdf), [blog](http://muratbuffalo.blogspot.tw/2017/07/a-comparison-of-distributed-machine.html)]

Uncategorized DL Discussion
===
1. [(Deep Learning’s Deep Flaws)’s Deep Flaws](https://www.kdnuggets.com/2015/01/deep-learning-flaws-universal-machine-learning.html) (Read the discussion in comments)
2. Jason Yosinski, Jeff Clune, et al. **Understanding Neural Networks Through Deep Visualization**. *ICML Deep Learning Workshop*, 2015. [[paper](http://yosinski.com/media/papers/Yosinski__2015__ICML_DL__Understanding_Neural_Networks_Through_Deep_Visualization__.pdf)]

Deep Learning vs. Human Learning
===
1. [Talk] Yann LeCun - [How does the brain learn so much so quickly?](https://www.youtube.com/watch?v=cWzi38-vDbE&feature=youtu.be&app=desktop) (CCN 2017)
2. [Talk] Yoshua Bengio - [Deep learning and Backprop in the Brain](https://www.youtube.com/watch?v=W86H4DpFnLY) (CCN 2017)
3. Demis Hassabis, et al. **Neuroscience-Inspired Artificial Intelligence**. *Journal of Neuron,* 2017. [[paper](https://deepmind.com/documents/113/Neuron.pdf)]
- [AI and Neuroscience: A virtuous circle](https://deepmind.com/blog/ai-and-neuroscience-virtuous-circle/)
- [Google’s AI Guru Says That Great Artificial Intelligence Must Build on Neuroscience](https://www.technologyreview.com/s/608317/googles-ai-guru-says-that-great-artificial-intelligence-must-build-on-neuroscience/) (MIT Technology Review)
- [Can This Man Make AI More Human?](https://www.technologyreview.com/s/544606/can-this-man-make-ai-more-human/) (MIT Technology Review)
4. Jeff Hawkins, Subutai Ahmad and Yuwei Cui. **A Theory of How Columns in the Neocortex Enable Learning the Structure of the World**. *Frontiers in Neural Circuits*, 2017. [[paper](https://www.frontiersin.org/articles/10.3389/fncir.2017.00081/full)]

To be categorized
===
- [Lessons Learned Reproducing a Deep Reinforcement Learning Paper](http://amid.fish/reproducing-deep-rl)
- [Neural Network Design 2dn Edition](http://hagan.okstate.edu/NNDesign.pdf)