KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#developer-tools #typescript #dsh #dsh-plugin #ai-agent #codex #ai-agents #deepseek-harness #claude-code #cli #react #mcp

DeepLearning-Resources

★ 32 Open GitHub ↗

An awesome list of deep learning resources

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# DeepLearning-Resources
An awesome list of deep learning resources

## Table of Contents
* **[Tutorials / Courses](#tutorials-and-courses)**

* **[Talks / Videos](#talks-and-videos)**

* **[Books](#books)**

* **[Recent Papers](#recent-papers)**

* **[Recent Interesting Blog Posts](#recent-interesting-blog-posts)**

* **[Papers](#papers)**

* [Fundamentals](#fundamentals)
 * [Practical Tricks](#practical-tricks)
 * [Autoencoders](#autoencoders)
 * [Convolutional Neural Networks](#convolutional-neural-networks)
 * [Recurrent Neural Networks](#recurrent-neural-networks)
 * [Image Classification](#image-classification)
 * [Visualization and Inversion](#visualization-and-inversion)
 * [Object Detection](#object-detection)
 * [Semantic Segmentation](#semantic-segmentation)
 * [Optimization](#optimization)
 * [Regularization](#regularization)
 * [Reinforcement Learning](#reinforcement-learning)
 * [Architectures and other Applications](#architectures-and-other-applications)
 * [Parallelizing Deep Networks](#parallelizing-deep-networks)
 * [Neural Program Synthesis and Language Grounding](#neural-program-synthesis-and-language-grounding)

* **[Resources and References](#resources-and-references)**

### Tutorials and Courses

* [Generative Adversarial Networks](https://arxiv.org/pdf/1701.00160v1.pdf) by Ian Goodfellow, NIPS 2016 Tutorial.
* [Deep Learning for Computer Vision](https://www.youtube.com/watch?v=qgx57X0fBdA)  by Rob Fergus, NIPS 2013 tutorial.
* [Convolutional Neural Networks for Visual Recognition](http://vision.stanford.edu/teaching/cs231n/syllabus.html) by Fei-Fei Li, Andrej Karpathy and Justin Johnson, 2016. [Stanford]
* [Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/) by Richard Socher. [Stanford]
* [Neural Networks for Machine Learning](https://www.coursera.org/course/neuralnets) by Geoffrey Hinton, 2012. [Coursera]
* [Lecture notes](http://www.cs.toronto.edu/~hinton/csc2535/lectures.html) from Geoffrey Hinton's classes on neural networks 2013.
* [Deep Learning Course](http://www.computervisiontalks.com/tag/deep-learning-course/) by Nando de Freitas, 2015. [Oxford]
* [Deep Learning Course](http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start) [CILVR lab, NYU]
* [Deep Learning](https://www.udacity.com/course/deep-learning--ud730) by Vincent Vanhoucke and Arpan Chakraborty, 2016. [Udacity/Google]
* [A Deep Learning Tutorial: From Perceptrons to Deep Networks](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks) by Ivan Vasilev.

###  Talks and Videos

* [The Next Generation of Neural Networks](https://www.youtube.com/watch?v=AyzOUbkUf3M) by Geoffrey Hinton at GoogleTechTalks 2007.
* [Recent Developments in Deep Learning](https://www.youtube.com/watch?v=vShMxxqtDDs) by Geoffrey Hinton, UBC 2013.
* [The Unreasonable Effectiveness of Deep Learning](https://www.youtube.com/watch?v=sc-KbuZqGkI) by Yann LeCun, Johns Hopkins University 2014.
* [Deep Learning of Representations](https://www.youtube.com/watch?v=4xsVFLnHC_0) by Yoshua Bengio at GoogleTechTalks 2012.
* [A Beginners Guide to Deep Neural Networks](http://googleresearch.blogspot.com/2015/09/a-beginners-guide-to-deep-neural.html) by Natalie Hammel and Lorraine Yurshansky, Google Research Blog 2015.
* [Deep Learning RNNaissance](https://www.youtube.com/watch?v=6bOMf9zr7N8) with Dr. Juergen Schmidhuber at NYC ML Meetup 2014.
* [Deep Learning: Theoretical Motivations](http://videolectures.net/deeplearning2015_bengio_theoretical_motivations/) by Yoshua Bengio, DLSS 2015.
* [Deep Learning for Vision: Tricks of the trade](https://www.youtube.com/watch?v=clgMTk5V2Sk) Facebook, Bay Area Vision Meeting 2013.
* [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) by Andrew Ng, Graduate Summer School: Deep Learning, Feature Learning 2013.

<h3>Books</h3>

* [Deep Learning](http://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
* [Deep Learning survey](https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf) in Nature by Yann LeCun, Yoshua Bengio, and Geoffrey Hinton.

###  Recent Papers

<h4>2016-12</h4>
* [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://arxiv.org/pdf/1612.01887v1.pdf) J Lu, C Xiong, D Parikh, R Socher - arXiv preprint arXiv:1612.01887, 2016

###  Recent Interesting Blog Posts

* [Yes You Should Understand Backprop ](https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b#.pokoany6j) by Andrej Karpathy
* [New Perspectives on Adversarial Training (NIPS 2016 workshop)](http://www.inference.vc/my-summary-of-adversarial-training-nips-workshop/) by Ferenc Huszár 
* [The major advancements in Deep Learning in 2016](https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/)
* [Demystifying Deep Reinforcement Learning](https://www.nervanasys.com/demystifying-deep-reinforcement-learning/) by Tambet Matiisen
* [Attention and Memory in Deep Learning and NLP](http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/)
* [Attention and Augmented Recurrent Neural Networks](http://distill.pub/2016/augmented-rnns/) by Chris Olah and Shan Carter
* [Why Randomness is Important for Deep Learning](http://blog.evjang.com/2016/07/randomness-deep-learning.html) by Eric Jang
* [Introduction to Neural Machine Translation with GPUs](https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-gpus-part-3/) by Kyunghyun Cho
* [An overview of gradient descent optimization algorithms](http://sebastianruder.com/optimizing-gradient-descent/index.html) by Sebastian Ruder

### Programming

#### Tensorflow

* [Deep Learning and Tensorflow](http://www.hvass-labs.org/) by Hvass laboratories
* [Sirajology](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A) by Siraj Raval

###  Papers

####  Fundamentals

* [Efficient BackProp,](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) LeCun YA, Bottou L, Orr GB, Müller KR. InNeural networks: Tricks of the trade 2012.
* [Learning representations by back-propagating errors,](http://www.iro.umontreal.ca/~pift6266/A06/refs/backprop_old.pdf) The original article on back-propagation, DE Rumelhart, GE Hinton, RJ Williams - Cognitive modeling, 1988.
* [Minimizing description length in an unsupervised neural network,](http://www.cs.toronto.edu/~hinton/absps/mdlnn.pdf) Hinton GE, Zemel RS. Minimizing description length in an unsupervised neural network. Preprint. 1997.
* [Dimensionality Reduction by Learning an Invariant Mapping,](http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf) Hadsell R, Chopra S, LeCun Y, CVPR 2006.

####  Practical Tricks

* [On the Importance of Initialization and Momentum in Deep Learning,](http://www.cs.utoronto.ca/~ilya/pubs/2013/1051_2.pdf) Sutskever I, Martens J, Dahl G, Hinton G, ICML 2013.
* [Understanding the Difficulty of Training Deep Feedforward Neural Networks,](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf) Glorot X, Bengio Y, In International conference on artificial intelligence and statistics 2010.
* [Practical Recommendations for Gradient-Based Training of Deep Architectures,](http://arxiv.org/abs/1206.5533) Y Bengio - Neural Networks: Tricks of the Trade, 2012 - Springer.
* [Improving Neural Networks by Preventing Co-adaptation of Feature Detectors,](http://arxiv.org/pdf/1207.0580v1) Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR, arXiv preprint arXiv:1207.0580, 2012.
* [Learning Deep Architectures for AI,](https://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf) Y Bengio - Foundations and trends in Machine Learning, 2009.

####  Autoencoders

* [Reducing the dimensionality of data with neural networks,](https://www.cs.toronto.edu/~hinton/science.pdf) GE Hinton, RR Salakhutdinov - Science, 2006.
* [Disentangling factors of variation for facial expression recognition,](http://www-etud.iro.umontreal.ca/%7Erifaisal/material/rifai_eccv_2012.pdf) S Rifai, Y Bengio, A Courville, P Vincent, M Mirza, ECCV 2012.
* [Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,](http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf) Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA, JMLR 2010.

####  Convolutional Neural Networks

* [Gradient-Based Learning Applied to Document Recognition,](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) LeCun Y, Bottou L, Bengio Y, Haffner P, Proceedings of the IEEE. 1998.
* [Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis,](http://131.107.65.14/pubs/68920/icdar03.pdf) Simard PY, Steinkraus D, Platt JC, ICDAR 2003.
* [Network in Network,](http://arxiv.org/abs/1312.4400) M Lin, Q Chen, S Yan - arXiv preprint arXiv:1312.4400, 2013.

<h4>Recurrent Neural Networks</h4>

* [Training Recurrent Neural Networks,](http://www.cs.utoronto.ca/%7Eilya/pubs/ilya_sutskever_phd_thesis.pdf) Ilya Sutskever, PhD Thesis, 2012.
* [Supervised Sequence Labelling with Recurrent Neural Networks,](https://www.cs.toronto.edu/%7Egraves/preprint.pdf) Textbook, Studies in Computational Intelligence, Springer, 2012.
* [Training and Analyzing Deep Recurrent Neural Networks,](http://papers.nips.cc/paper/5166-training-and-analysing-deep-recurrent-neural-networks.pdf) M Hermans, B Schrauwen, NIPS 2013.
* [Generating Sequences With Recurrent Neural Networks,](http://arxiv.org/abs/1308.0850) A Graves - arXiv preprint arXiv:1308.0850, 2013.

<h4>Image Classification</h4>

* [ImageNet Classification with Deep Convolutional Neural Networks,](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, NIPS 2012.
* [Very Deep Convolutional Networks for Large-Scale Image Recognition,](http://arxiv.org/abs/1409.1556) K Simonyan, A Zisserman - arXiv preprint arXiv:1409.1556, 2014.
* [Going Deeper with Convolutions,](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43022.pdf) Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew, CVPR 2015. 
* [Deep Residual Learning for Image Recognition,](https://arxiv.org/abs/1512.03385) K He, X Zhang, S Ren, J Sun - arXiv preprint arXiv:1512.03385, 2015.
* [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,](https://arxiv.org/abs/1502.01852) He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian, ICCV 2015.

<h4>Visualization and Inversion</h4>

* [Visualizing and Understanding Convolutional Networks,](https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiRnoiSy-_MAhVML48KHWKSAd0QFggpMAE&url=https%3A%2F%2Farxiv.org%2Fabs%2F1311.2901&usg=AFQjCNE3Q7HlhHswHMe0Wq3zim3sFpj0Yg&sig2=6cRr9PeP74CnLX7N_EEtjw&bvm=bv.122448493,d.c2I) MD Zeiler, R Fergus, ECCV 2014.
* [Understanding Deep Image Representations by Inverting Them,](http://arxiv.org/abs/1412.0035) A Mahendran, A Vedaldi, CVPR 2015.
* [Visualizing and Understanding Recurrent Networks,](https://arxiv.org/abs/1506.02078) A Karpathy, J Johnson, FF Li - arXiv preprint arXiv:1506.02078, 2015.

<h4>Object Detection</h4>

* [Fast R-CNN,](https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwi1jd-nyu_MAhXFqI8KHZiABRkQFggxMAI&url=http%3A%2F%2Farxiv.org%2Fabs%2F1504.08083&usg=AFQjCNHAzLwa0vABnH_RuO7yizN40ijrHA&sig2=7_wKcrrvb3B3yNlnfM-u5A) R Girshick, ICCV 2015.
* [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,](http://arxiv.org/abs/1506.01497) S Ren, K He, R Girshick, J Sun, NIPS 2015.
* [Region-based Convolutional Networks for Accurate Object Detection and Segmentation,](https://www.cs.berkeley.edu/%7Erbg/papers/pami/rcnn_pami.pdf) R Girshick, J Donahue, T Darrell, J Malik, PAMI 2016.

<h4>Semantic Segmentation</h4>

* [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs,](https://arxiv.org/pdf/1412.7062v3.pdf) Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL, arXiv preprint arXiv:1412.7062. 2014.
* [Fully Convolutional Networks for Semantic Segmentation,](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf) J Long, E Shelhamer, T Darrell, CVPR 2015.
* [Semantic Image Segmentation via Deep Parsing Network,](http://arxiv.org/pdf/1509.02634.pdf) Z Liu, X Li, P Luo, CC Loy, X Tang, ICCV 2015.
* [Conditional Random Fields as Recurrent Neural Networks,](http://www.robots.ox.ac.uk/~szheng/CRFasRNN.html) Zheng S, Jayasumana S, Romera-Paredes B, Vineet V, Su Z, Du D, Huang C, Torr PH, ICCV 2015.

<h4>Optimization</h4>

* [Deep learning via Hessian-free optimization,](http://icml2010.haifa.il.ibm.com/papers/458.pdf) Martens J, ICML 2010.
* [On optimization Methods for Deep Learning,](http://ai.stanford.edu/~quocle/LeNgiCoaLahProNg11.pdf) Ngiam J, Coates A, Lahiri A, Prochnow B, Le QV, Ng AY, ICML 2011.
* [Adam: A Method for Stochastic Optimization,](http://arxiv.org/abs/1412.6980) D Kingma, J Ba - arXiv preprint arXiv:1412.6980, 2014.
* [On the saddle point problem for non-convex optimization,](http://arxiv.org/abs/1405.4604) R Pascanu, YN Dauphin, S Ganguli, Y Bengio - arXiv preprint arXiv:1405.4604, 2014
* [Identifying and attacking the saddle point problem in high-dimensional non-convex optimization,](http://arxiv.org/abs/1406.2572) Dauphin YN, Pascanu R, Gulcehre C, Cho K, Ganguli S, Bengio Y, NIPS 2014.
* [The Loss Surfaces of Multilayer Networks,](http://arxiv.org/abs/1412.0233) Choromanska A, Henaff M, Mathieu M, Arous GB, LeCun Y, arXiv preprint arXiv:1412.0233. 2014.
* [Gradient-based Hyperparameter Optimization through Reversible Learning,](http://arxiv.org/pdf/1502.03492v3.pdf) D Maclaurin, D Duvenaud, RP Adams - arXiv preprint arXiv:1502.03492, 2015.

<h4>Regularization</h4>

* [Rectified Linear Units Improve Restricted Boltzmann Machines,](http://www.cs.toronto.edu/%7Efritz/absps/reluICML.pdf) Nair V, Hinton GE, ICML 2010.
* [Maxout Networks,](http://jmlr.org/proceedings/papers/v28/goodfellow13.pdf) Goodfellow IJ, Warde-Farley D, Mirza M, Courville A, Bengio Y, arXiv preprint arXiv:1302.4389. 2013.
* [Dropout: A Simple Way to Prevent Neural Networks from Overfitting,](https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&uact=8&ved=0ahUKEwjapeOt3-_MAhWDQI8KHYyXDtwQFggpMAM&url=https%3A%2F%2Fwww.cs.toronto.edu%2F%7Ehinton%2Fabsps%2FJMLRdropout.pdf&usg=AFQjCNFModVeeXkqtxn_TXeKPB0zFtw5ew&sig2=sx2fvjDBZ9HGRDIgq-ArgA&bvm=bv.122448493,d.c2I) Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R, JMLR 2014.
* [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,](http://arxiv.org/abs/1502.03167) S Ioffe, C Szegedy - arXiv preprint arXiv:1502.03167, 2015.

<h4>Reinforcement Learning</h4>

* [Human-level control through deep reinforcement learning,](http://rdcu.be/cdlg) Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Nature 2015.
* [Playing Atari with Deep Reinforcement Learning,](http://arxiv.org/abs/1312.5602) Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M, arXiv preprint arXiv:1312.5602. 2013.

<h4>Architectures and other Applications</h4>

* [Neural network Zoo,](http://www.asimovinstitute.org/neural-network-zoo/) Fjodor van Veen, Asimov Institute
* [DeepFace: Closing the Gap to Human-Level Performance in Face Verification,](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf) Taigman Y, Yang M, Ranzato MA, Wolf L, CVPR 2014.					
* [Large Scale Distributed Deep Networks,](https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&cad=rja&uact=8&ved=0ahUKEwjzitmTyO_MAhVHM48KHT4cDdkQFgg8MAQ&url=https%3A%2F%2Fpapers.nips.cc%2Fpaper%2F4687-large-scale-distributed-deep-networks.pdf&usg=AFQjCNG-NwddgOGxTKKWaJql4wUC-Rl1rw&sig2=Al564_8r9oxXrp2byl-TIw) Dean J, Corrado G, Monga R, Chen K, Devin M, Mao M, Senior A, Tucker P, Yang K, Le QV, Ng AY, NIPS 2012.
* [Predicting Parameters in Deep Learning,](http://www.cs.toronto.edu/%7Eranzato/publications/denil_nips2013.pdf) Denil M, Shakibi B, Dinh L, de Freitas N, NIPS 2013
* [Distilling the Knowledge in a Neural Network,](http://arxiv.org/abs/1503.02531) G Hinton, O Vinyals, J Dean, arXiv preprint arXiv:1503.02531, 2015.
* [Spatial Transformer Networks,](http://arxiv.org/pdf/1506.02025v3.pdf) Jaderberg M, Simonyan K, Zisserman A, NIPS 2015.
* [Generative Adversarial Networks,](http://arxiv.org/abs/1406.2661) Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y, NIPS 2014.
* [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,](http://arxiv.org/abs/1511.06434) A Radford, L Metz, S Chintala, arXiv preprint arXiv:1511.06434, 2015.
* [Semi-Supervised Learning with Ladder Networks,](http://arxiv.org/abs/1507.02672) A Rasmus, M Berglund, M Honkala, H Valpola, T Raiko, NIPS 2015.
* [Highway Networks,](http://arxiv.org/pdf/1505.00387v2.pdf) Srivastava RK, Greff K, Schmidhuber J, arXiv preprint arXiv:1505.00387, 2015.
* [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,](https://arxiv.org/pdf/1406.4729v4.pdf) He K, Zhang X, Ren S, Sun J, PAMI 2015.

<h4>Parallelizing Deep Networks</h4>

* [One weird trick for parallelizing convolutional neural networks,](https://arxiv.org/pdf/1404.5997v2.pdf) A Krizhevsky - arXiv preprint arXiv:1404.5997, 2014.
* [Large Scale Distributed Deep Networks,](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/40565.pdf) Dean, Jeffrey, et al. Advances in neural information processing systems. 2012.
* [Coarse Grain Parallelization of Deep Neural Networks,](http://delivery.acm.org/10.1145/2860000/2851158/a1-tallada.pdf?ip=14.139.82.6&id=2851158&acc=ACTIVE%20SERVICE&key=045416EF4DDA69D9%2E1E2B3508530718A8%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=711699610&CFTOKEN=31870658&__acm__=1483378680_7cecabfe09da7f1e8f00d280130783a8) Tallada, M. G. In Proceedings of the 21st ACM SIGPLAN 2016.

<h4>Neural Program Synthesis and Language Grounding</h4>

* [DEEPCODER: LEARNING TO WRITE PROGRAMS](https://arxiv.org/pdf/1611.01989.pdf)
* [Program Synthesis using Natural Language](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/07/NLPSynthesis.pdf)
* [Programming with a Differentiable Forth Interpreter](https://arxiv.org/pdf/1605.06640.pdf)
* [Unsupervised Learning by Program Synthesis](https://papers.nips.cc/paper/5785-unsupervised-learning-by-program-synthesis.pdf)
* [Differentiable Programs with Neural Libraries](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/03/main2.pdf)
* [The symbol grounding problem](http://courses.media.mit.edu/2004spring/mas966/Harnad%20symbol%20grounding.pdf)
* [Language to Logical Form with Neural Attention](https://arxiv.org/pdf/1601.01280.pdf) 
* [Deep Neural Networks with Massive Learned Knowledge](https://www.cs.cmu.edu/~rsalakhu/papers/emnlp16deep.pdf)
* [Harnessing Deep Neural Networks with Logic Rules](http://www.petuum.com/pdf/Hu_etal_ACL16.pdf)
* [End-To-End Memory Networks](https://papers.nips.cc/paper/5846-end-to-end-memory-networks.pdf)
* [Reinforcement Learning for Mapping Instructions to Actions](http://aclweb.org/anthology/P09-1010)
* [Parsing with Compositional Vector Grammars](http://www.aclweb.org/anthology/P13-1045)

<h3>Resources and References</h3>

* [Deep Learning Summer School,](http://videolectures.net/deeplearning2015_montreal/) Montreal 2015.
* [LISA reading List](http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/ReadingOnDeepNetworks)
* [Deep learning resources](http://deeplearning.net/reading-list/)
* [Awesome Deep Vision](https://github.com/kjw0612/awesome-deep-vision) - a curated list of deep learning resources for computer vision.
* [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning) 
* [Language Grounding to Vision and Control](https://katefvision.github.io/LanguageGrounding/#readings)

DeepLearning-Resources

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README