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+
+### Survey Review
+- Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton [[pdf]](https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf) :sparkles:
+- Deep learning in neural networks: An overview (2015), J. Schmidhuber [[pdf]](http://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf) :sparkles:
+- Representation learning: A review and new perspectives (2013), Y. Bengio et al. [[pdf]](http://www.cl.uni-heidelberg.de/courses/ws14/deepl/BengioETAL12.pdf) :sparkles:
+
+### Theory Future
+- Distilling the knowledge in a neural network (2015), G. Hinton et al. [[pdf]](http://arxiv.org/pdf/1503.02531)
+- Deep neural networks are easily fooled: High confidence predictions for unrecognizable images (2015), A. Nguyen et al. [[pdf]](http://arxiv.org/pdf/1412.1897)
+- How transferable are features in deep neural networks? (2014), J. Yosinski et al. *(Bengio)* [[pdf]](http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-neural-networks.pdf)
+- Why does unsupervised pre-training help deep learning (2010), E. Erhan et al. *(Bengio)* [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf)
+- Understanding the difficulty of training deep feedforward neural networks (2010), X. Glorot and Y. Bengio [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_GlorotB10.pdf)
+
+### Optimization Regularization
+- Taking the human out of the loop: A review of bayesian optimization (2016), B. Shahriari et al. [[pdf]](https://www.cs.ox.ac.uk/people/nando.defreitas/publications/BayesOptLoop.pdf)
+- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (2015), S. Loffe and C. Szegedy [[pdf]](http://arxiv.org/pdf/1502.03167) :sparkles:
+- Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015), K. He et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf) :sparkles:
+- Dropout: A simple way to prevent neural networks from overfitting (2014), N. Srivastava et al. *(Hinton)* [[pdf]](http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf) :sparkles:
+- Adam: A method for stochastic optimization (2014), D. Kingma and J. Ba [[pdf]](http://arxiv.org/pdf/1412.6980)
+- Regularization of neural networks using dropconnect (2013), L. Wan et al. *(LeCun)* [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/icml2013_wan13.pdf)
+- Improving neural networks by preventing co-adaptation of feature detectors (2012), G. Hinton et al. [[pdf]](http://arxiv.org/pdf/1207.0580.pdf) :sparkles:
+- Spatial pyramid pooling in deep convolutional networks for visual recognition (2014), K. He et al. [[pdf]](http://arxiv.org/pdf/1406.4729)
+- Random search for hyper-parameter optimization (2012) J. Bergstra and Y. Bengio [[pdf]](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a)
+
+### NetworkModels
+- Deep residual learning for image recognition (2016), K. He et al. *(Microsoft)* [[pdf]](http://arxiv.org/pdf/1512.03385) :sparkles:
+- Going deeper with convolutions (2015), C. Szegedy et al. *(Google)* [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf) :sparkles:
+- Fast R-CNN (2015), R. Girshick [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf) :sparkles:
+- Very deep convolutional networks for large-scale image recognition (2014), K. Simonyan and A. Zisserman [[pdf]](http://arxiv.org/pdf/1409.1556) :sparkles:
+- Fully convolutional networks for semantic segmentation (2015), J. Long et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf) :sparkles:
+- OverFeat: Integrated recognition, localization and detection using convolutional networks (2014), P. Sermanet et al. *(LeCun)* [[pdf]](http://arxiv.org/pdf/1312.6229)
+- Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus [[pdf]](http://arxiv.org/pdf/1311.2901) :sparkles:
+- Maxout networks (2013), I. Goodfellow et al. *(Bengio)* [[pdf]](http://arxiv.org/pdf/1302.4389v4)
+- ImageNet classification with deep convolutional neural networks (2012), A. Krizhevsky et al. *(Hinton)* [[pdf]](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) :sparkles:
+- Large scale distributed deep networks (2012), J. Dean et al. [[pdf]](http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks.pdf) :sparkles:
+- Deep sparse rectifier neural networks (2011), X. Glorot et al. *(Bengio)* [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_GlorotBB11.pdf)
+
+### Image
+- Imagenet large scale visual recognition challenge (2015), O. Russakovsky et al. [[pdf]](http://arxiv.org/pdf/1409.0575) :sparkles:
+- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2015), S. Ren et al. [[pdf]](http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf)
+- DRAW: A recurrent neural network for image generation (2015), K. Gregor et al. [[pdf]](http://arxiv.org/pdf/1502.04623)
+- Rich feature hierarchies for accurate object detection and semantic segmentation (2014), R. Girshick et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf)
+- Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Oquab_Learning_and_Transferring_2014_CVPR_paper.pdf)
+- DeepFace: Closing the Gap to Human-Level Performance in Face Verification (2014), Y. Taigman et al. *(Facebook)* [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf)
+- Decaf: A deep convolutional activation feature for generic visual recognition (2013), J. Donahue et al. [[pdf]](http://arxiv.org/pdf/1310.1531)
+- Learning Hierarchical Features for Scene Labeling (2013), C. Farabet et al. *(LeCun)* [[pdf]](https://hal-enpc.archives-ouvertes.fr/docs/00/74/20/77/PDF/farabet-pami-13.pdf)
+- Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis (2011), Q. Le et al. [[pdf]](http://robotics.stanford.edu/~wzou/cvpr_LeZouYeungNg11.pdf)
+- Learning mid-level features for recognition (2010), Y. Boureau *(LeCun)* [[pdf]](http://ece.duke.edu/~lcarin/boureau-cvpr-10.pdf)
+
+### Caption
+- Show, attend and tell: Neural image caption generation with visual attention (2015), K. Xu et al. *(Bengio)* [[pdf]](http://arxiv.org/pdf/1502.03044) :sparkles:
+- Show and tell: A neural image caption generator (2015), O. Vinyals et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vinyals_Show_and_Tell_2015_CVPR_paper.pdf) :sparkles:
+- Long-term recurrent convolutional networks for visual recognition and description (2015), J. Donahue et al. [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf) :sparkles:
+- Deep visual-semantic alignments for generating image descriptions (2015), A. Karpathy and L. Fei-Fei [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Karpathy_Deep_Visual-Semantic_Alignments_2015_CVPR_paper.html) :sparkles:
+
+
+### Video HumanActivity
+- Large-scale video classification with convolutional neural networks (2014), A. Karpathy et al. *(FeiFei)* [[pdf]](vision.stanford.edu/pdf/karpathy14.pdf)
+- A survey on human activity recognition using wearable sensors (2013), O. Lara and M. Labrador [[pdf]](http://romisatriawahono.net/lecture/rm/survey/computer%20vision/Lara%20-%20Human%20Activity%20Recognition%20-%202013.pdf)
+- 3D convolutional neural networks for human action recognition (2013), S. Ji et al. [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/icml2010_JiXYY10.pdf)
+- Deeppose: Human pose estimation via deep neural networks (2014), A. Toshev and C. Szegedy [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Toshev_DeepPose_Human_Pose_2014_CVPR_paper.pdf)
+- Action recognition with improved trajectories (2013), H. Wang and C. Schmid [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Wang_Action_Recognition_with_2013_ICCV_paper.pdf)
+
+### WordEmbedding
+- Glove: Global vectors for word representation (2014), J. Pennington et al. [[pdf]](http://llcao.net/cu-deeplearning15/presentation/nn-pres.pdf) :sparkles:
+- Sequence to sequence learning with neural networks (2014), I. Sutskever et al. [[pdf]](http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf)
+- Distributed representations of sentences and documents (2014), Q. Le and T. Mikolov [[pdf]](http://arxiv.org/pdf/1405.4053) *(Google)* :sparkles:
+- Distributed representations of words and phrases and their compositionality (2013), T. Mikolov et al. *(Google)* [[pdf]](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) :sparkles:
+- Efficient estimation of word representations in vector space (2013), T. Mikolov et al. *(Google)* [[pdf]](http://arxiv.org/pdf/1301.3781) :sparkles:
+- Word representations: a simple and general method for semi-supervised learning (2010), J. Turian *(Bengio)* [[pdf]](http://www.anthology.aclweb.org/P/P10/P10-1040.pdf)
+
+### MachineTranslation QnA
+- Towards ai-complete question answering: A set of prerequisite toy tasks (2015), J. Weston et al. [[pdf]](http://arxiv.org/pdf/1502.05698)
+- Neural machine translation by jointly learning to align and translate (2014), D. Bahdanau et al. *(Bengio)* [[pdf]](http://arxiv.org/pdf/1409.0473) :sparkles:
+- Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014), K. Cho et al. *(Bengio)* [[pdf]](http://arxiv.org/pdf/1406.1078)
+- A convolutional neural network for modelling sentences (2014), N. kalchbrenner et al. [[pdf]](http://arxiv.org/pdf/1404.2188v1)
+- Convolutional neural networks for sentence classification (2014), Y. Kim [[pdf]](http://arxiv.org/pdf/1408.5882)
+- The stanford coreNLP natural language processing toolkit (2014), C. Manning et al. [[pdf]](http://www.surdeanu.info/mihai/papers/acl2014-corenlp.pdf)
+- Recursive deep models for semantic compositionality over a sentiment treebank (2013), R. Socher et al. [[pdf]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.383.1327&rep=rep1&type=pdf) :sparkles:
+- Natural language processing (almost) from scratch (2011), R. Collobert et al. [[pdf]](http://arxiv.org/pdf/1103.0398)
+- Recurrent neural network based language model (2010), T. Mikolov et al. [[pdf]](http://www.fit.vutbr.cz/research/groups/speech/servite/2010/rnnlm_mikolov.pdf)
+
+### Speech Etc.
+- Speech recognition with deep recurrent neural networks (2013), A. Graves *(Hinton)* [[pdf]](http://arxiv.org/pdf/1303.5778.pdf)
+- Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups (2012), G. Hinton et al. [[pdf]](http://www.cs.toronto.edu/~asamir/papers/SPM_DNN_12.pdf) :sparkles:
+- Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition (2012) G. Dahl et al. [[pdf]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.337.7548&rep=rep1&type=pdf) :sparkles:
+
+### RL Robotics
+- Mastering the game of Go with deep neural networks and tree search, D. Silver et al. *(DeepMind)* [[pdf]](Mastering the game of Go with deep neural networks and tree search)
+- Human-level control through deep reinforcement learning (2015), V. Mnih et al. *(DeepMind)* [[pdf]](http://www.davidqiu.com:8888/research/nature14236.pdf) :sparkles:
+- Deep learning for detecting robotic grasps (2015), I. Lenz et al. [[pdf]](http://www.cs.cornell.edu/~asaxena/papers/lenz_lee_saxena_deep_learning_grasping_ijrr2014.pdf)
+- Playing atari with deep reinforcement learning (2013), V. Mnih et al. *(DeepMind)* [[pdf]](http://arxiv.org/pdf/1312.5602.pdf))
+
+### Unsupervised
+- Building high-level features using large scale unsupervised learning (2013), Q. Le et al. [[pdf]](http://arxiv.org/pdf/1112.6209) :sparkles:
+- Contractive auto-encoders: Explicit invariance during feature extraction (2011), S. Rifai et al. *(Bengio)* [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/ICML2011Rifai_455.pdf)
+- An analysis of single-layer networks in unsupervised feature learning (2011), A. Coates et al. [[pdf]](http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_CoatesNL11.pdf)
+- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al. *(Bengio)* [[pdf]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484&rep=rep1&type=pdf)
+- A practical guide to training restricted boltzmann machines (2010), G. Hinton [[pdf]](http://www.csri.utoronto.ca/~hinton/absps/guideTR.pdf)
+- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion (2010), P. Vincent et al. *(Bengio)* [[pdf]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.297.3484&rep=rep1&type=pdf)
+
+### Hardware Software
+- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016), M. Abadi et al. *(Google)* [[pdf]](http://arxiv.org/pdf/1603.04467)
+- MatConvNet: Convolutional neural networks for matlab (2015), A. Vedaldi and K. Lenc [[pdf]](http://arxiv.org/pdf/1412.4564)
+- Caffe: Convolutional architecture for fast feature embedding (2014), Y. Jia et al. [[pdf]](http://arxiv.org/pdf/1408.5093) :sparkles:
+- Theano: new features and speed improvements (2012), F. Bastien et al. *(Bengio)* [[pdf]](http://arxiv.org/pdf/1211.5590)
+
+## License
+[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)
diff --git a/README.md b/README.md
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+++ b/README.md
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+This is a fork of the PDF version of Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville. I've added EPUB and MOBI versions of the book, generated from the PDF.
+
+Once you get your deep learning career on the roll, consider purchasing a printed copy of this generously released work by the respective authors.
+
 # DeepLearningBook
 MIT Deep Learning Book in PDF format
 
 This book was downloaded in HTML form and conviniently joined as a single PDF file for your enjoyment. Please notice the known issues in the web page, especially with regards to some symbols not rendering well or not at all.
 
+This is NOT a perfect copy of the book and it is not meant to be, it is just a printout from the author's website, which some people have trouble performing. If you want the original source, please purchase it from Amazon (link below). I will not be fixing PDF by hand to make this perfect.
+
+PLEASE SUPPORT PROFESSOR IAN GOODFELLOW and the authors if you can purchase the paper book at Amazon. It is not expensive ($72) and probably contains content that is newer and without typographic mistakes.
+https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618
+
+Update: we just added a section [[Deep Learning Papers]](DeepLearningPapers.md) to this repository. We feel it complements very well the intention of this repository that is to help students to get all the relevant information about deep learning in one place.
+
+For questions regarding the contents of the book, we encourage you to ask them on the book's forum 
+https://groups.google.com/forum/#!forum/deeplearningbook
+
 From http://www.deeplearningbook.org/
 
     An MIT Press book
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