【最全开工干货】深度学习书单、文献及数据集（共446项）
新年伊始，相信每个人已经制定好了自己2016年的计划。随着无人机和智能机器人在春晚亮相，想必许多人会对“人工智能”、“机器学习”，“深度学习”这些科技热词充满了好奇。为此，新智元给众多热爱人工智能领域的读者准备了一份丰厚的大理。小编深知许多对人工智能领域感兴趣的读者可能还不知如何入手该领域，那么，小编建议就从了解深度学习开始吧！新智元为学习深度学习的初学者整理了一份非常全面的书单，下面就随小编一起来看看这份书单中包含哪些板块的内容呢？
给深度学习从业者的书单
一、关于矩阵或者单变量微积分计算的文献（共5项）
Introduction to Algorithms by Erik Demaine and Srinivas Devadas.
Single Variable Calculus by David Jerison.
Multivariable Calculus by Denis Auroux.
Differential Equations by Arthur Mattuck, Haynes Miller, Jeremy Orloff, John Lewis.
Linear Algebra by Gilbert Strang.
二、基于深度学习的计算理论，学习理论，神经科学等等（共12项）
Introduction to the Theory of Computation by Michael Sipser.
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig.
Pattern Recognition and Machine Learning by Christopher Bishop.
Machine Learning: A probabilistic perspective by Kevin Patrick Murphy.
CS229 Machine Learning Course Materials by Andrew Ng at Stanford University.
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.
Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and Nir Friedman.
Convex Optimization by Stephen Boyd and Lieven Vandenberghe.
An Introduction to Statistical Learning with application in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
Neuronal Dynamics: From single neurons to networks and models of cognition by Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski.
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Peter Dayan and Laurence F. Abbott.
Michael I. Jordan Reading List of Machine Learning at Hacker News.
三、关于深度学习基础知识的文献（共5项）
Deep Learning in Neural Networks: An Overview by Jürgen Schmidhuber.
Deep Learning Book by Yoshua Bengio, Ian Goodfellow and Aaron Courville.
Learning Deep Architectures for AI by Yoshua Bengio.
Representation Learning: A Review and New Perspectives by Yoshua Bengio, Aaron Courville, Pascal Vincent.
Reading lists for new LISA students by LISA Lab, University of Montreal.
四、关于深度学习的教材，实用手册以及有用的软件（共17项）
Machine Learning by Andrew Ng.
Neural Networks for Machine Learning by Geoffrey Hinton.
Deep Learning Tutorial by LISA Lab, University of Montreal.
Unsupervised Feature Learning and Deep Learning Tutorial by AI Lab, Stan ford University.
CS231n: Convolutional Neural Networks for Visual Recognition by Stanfor d Uiversity.
CS224d: Deep Learning for Natural Language Processing by Stanford Univer sity.
Theano by LISA Lab, University of Montreal.
PyLearn2 by LISA Lab, University of Montreal.
Caffe by Berkeley Vision and Learning Center (BVLC) and community contrib utor Yangqing Jia.
Torch 7
neon by Nervana.
cuDNN by NVIDIA.
ConvNetJS by Andrej Karpathy.
DeepLearning4j
Chainer: Neural network framework by Preferred Networks, Inc.
Blocks by LISA Lab, University of Montreal.
Fuel by LISA Lab, University of Montreal.
五、关于深度学习和特征学习的文献（共11项）
Automatic Speech Recognition - A Deep Learning Approach by Dong Yu an d Li Deng (Published by Springer, no Open Access)
Backpropagation Applied to Handwritten Zip Code Recognition by Y. LeCu n, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard an d L. D. Jackel.
Comparison of Training Methods for Deep Neural Networks by Patrick O. Glauner.
Deep Learning by Yann LeCun, Yoshua Bengio, Geoffrey Hinton. (NO FREE COPY AVAILABLE)
Distributed Representations of Words and Phrases and their Compositionality by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado and Jeffrey Dean.
Efficient Estimation of Word Representations in Vector Space by Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean.
Efficient Large Scale Video Classification by Balakrishnan Varadarajan, George Toderici, Sudheendra Vijayanarasimhan, Apostol Natsev.
Foundations and Trends in Signal Processing: DEEP LEARNING — Methods and Applications by Li Deng and Dong Yu.
From Frequency to Meaning: Vector Space Models of Semantics by Peter D. Turney and Patrick Pantel.
LSTM: A Search Space Odyssey by Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber.
Supervised Sequence Labelling with Recurrent Neural Networks by Alex Graves.
六、最近必读的关于深度学习领域的最新进展（共332项）
A Convolutional Attention Network for Extreme Summarization of Source Code by Miltiadis Allamanis, Hao Peng, Charles Sutton.
A Deep Bag-of-Features Model for Music Auto-Tagging by Juhan Nam, Jorge Herrera, Kyogu Lee.
A Deep Generative Deconvolutional Image Model by Yunchen Pu, Xin Yuan, Andrew Stevens, Chunyuan Li, Lawrence Carin.
A Deep Neural Network Compression Pipeline: Pruning, Quantization, Huffman Encoding by Song Han, Huizi Mao, William J. Dally.
A Deep Pyramid Deformable Part Model for Face Detection by Rajeev Ranjan, Vishal M. Patel, Rama Chellappa.
A Deep Siamese Network for Scene Detection in Broadcast Videos by Lorenzo Baraldi, Costantino Grana, Rita Cucchiara.
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion by Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob G. Simonsen, Jian-Yun Nie.
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification by Linjie Yang, Ping Luo, Chen Change Loy, Xiaoou Tang.
A Lightened CNN for Deep Face Representation by Xiang Wu, Ran He, Zhenan Sun.
A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction by Thomas Wiatowski, Helmut Bölcskei.
A Multi-scale Multiple Instance Video Description Network by Huijuan Xu, Subhashini Venugopalan, Vasili Ramanishka, Marcus Rohrbach, Kate Saenko.
A Recurrent Latent Variable Model for Sequential Data by Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, Yoshua Bengio.
A Restricted Visual Turing Test for Deep Scene and Event Understanding by Hang Qi, Tianfu Wu, Mun-Wai Lee, Song-Chun Zhu.
A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification by Ye Zhang, Byron Wallace.
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering by Kan Chen, Jiang Wang, Liang-Chieh Chen, Haoyuan Gao, Wei Xu, Ram Nevatia.
Accelerating Very Deep Convolutional Networks for Classification and Detection by Xiangyu Zhang, Jianhua Zou, Kaiming He, Jian Sun.
Accurate Image Super-Resolution Using Very Deep Convolutional Networks by Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee.
Action Recognition using Visual Attention by Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov.
Action Recognition With Trajectory-Pooled Deep-Convolutional Descriptors by Limin Wang, Yu Qiao, Xiaoou Tang.
Action-Conditional Video Prediction using Deep Networks in Atari Games by Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard Lewis, Satinder Singh.
Active Object Localization with Deep Reinforcement Learning by Juan C. Caicedo, Svetlana Lazebnik.
adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs by Nitish Shirish Keskar, Albert S. Berahas.
Adding Gradient Noise Improves Learning for Very Deep Networks by Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens.
Adversarial Autoencoders by Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow.
Adversarial Manipulation of Deep Representations by Sara Sabour, Yanshuai Cao, Fartash Faghri, David J. Fleet.
All you need is a good init by Dmytro Mishkin, Jiri Matas.
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition by Baoguang Shi, Xiang Bai, Cong Yao.
Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering by Xiaoqiang Zhou, Baotian Hu, Qingcai Chen, Buzhou Tang, Xiaolong Wang.
Anticipating the future by watching unlabeled video by Carl Vondrick, Hamed Pirsiavash, Antonio Torralba.
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering by Haoyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu.
Artificial Neural Networks Applied to Taxi Destination Prediction by Alexandre de Brébisson, Étienne Simon, Alex Auvolat, Pascal Vincent, Yoshua Bengio.
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering by Huijuan Xu, Kate Saenko.
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing by Ankit Kumar, Ozan Irsoy, Jonathan Su, James Bradbury, Robert English, Brian Pierce, Peter Ondruska, Ishaan Gulrajani, Richard Socher.
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources by Qi Wu, Peng Wang, Chunhua Shen, Anton van den Hengel, Anthony Dick.
Ask Your Neurons: A Neural-based Approach to Answering Questions about Images by Mateusz Malinowski, Marcus Rohrbach, Mario Fritz.
Associative Long Short-Term Memory by Ivo Danihelka, Greg Wayne, Benigno Uria, Nal Kalchbrenner, Alex Graves.
AttentionNet: Aggregating Weak Directions for Accurate Object Detection by Donggeun Yoo, Sunggyun Park, Joon-Young Lee, Anthony Paek, In So Kweon.
Attention-Based Models for Speech Recognition by Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, Yoshua Bengio.
Attention to Scale: Scale-aware Semantic Image Segmentation by Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, Alan L. Yuille.
Attention with Intention for a Neural Network Conversation Model by Kaisheng Yao, Geoffrey Zweig, Baolin Peng.
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery by Izhar Wallach, Michael Dzamba, Abraham Heifets.
七、数据集（共13项）
Caltech 101 by L. Fei-Fei, R. Fergus and P. Perona.
Caltech 256 by G. Griffin, AD. Holub, P. Perona.
CIFAR-10 by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.
CIFAR-100 by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.
The Comprehensive Cars (CompCars) dataset by Linjie Yang, Ping Luo, Chen Change Loy, Xiaoou Tang.
Flickr30k by Peter Young, Alice Lai, Micah Hodosh, Julia Hockenmaier.
ImageNet by Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei.
Microsoft COCO by Microsoft Research.
MNIST by Yann LeCun, Corinna Cortes, Christopher J.C. Burges.
Places by MIT Computer Science and Artificial Intelligence Laboratory.
STL-10 by Adam Coates, Honglak Lee, Andrew Y. Ng.
SVHN by Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng.
WWW Crowd Dataset by Jing Shao, Kai Kang, Chen Change Loy, and Xiaogang Wang.
八、关于学习深度学习的博客、访谈栏目等等（共4项）
Talking Machines hosted by Katherine Gorman and Ryan Adams.
Machine Learning & Computer Vision Talks by computervisiontalks.
How we’re teaching computers to understand pictures by Fei-Fei Li, Stanford University.
Deep Learning Community
九、亚马逊提供的用于深度学习的公共AMI网络服务（共3项）
DGYDLGPUv4 (ami-ba516ee8) [Based on g2.2xlarge]
DGYDLGPUXv1 (ami-52516e00) [Based on g2.8xlarge]
Caffe/CuDNN built 2015-05-04 (ami-763a331e) [For both g2.2xlarge and g2.8xlarge]
十、实用的深度神经网络—从GPU计算的角度来看（共26项）
Slides（8项）
Introduction
Python Platform for Scientific Computing
Theano Crash Course
Machine Learning Basics
Softmax Regression
Feedforward Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Practical tutorials（8项）
Python Warm-up, pre-processing
Feedforward Layer
Softmax Regression
Multi Layer Perceptron Network
Feedforward Model
Auto-encoder
Convolutional Neural Networks
Recurrent Neural Networks
Codes
Telauges （10项）
A new deep learning library for learning DL.
MLP Layers: Tanh Layer, Sigmoid Layer, Identity Layer, ReLU Layer
Softmax Regression
ConvNet layers: Tanh Layer, Sigmoid Layer, Identity Layer, ReLU Layer
Max-Pooling layer
Max-Pooling same size
Feedforward Model
Auto-Encoder Model
SGD, Adagrad, Adadelta, RMSprop, Adam
Dropout
看到这里，各位热爱深度学习的狂粉们是不是也和小编一样想将这些宝贝资料收藏起来呢？那么，下面再跟随小编来看看怎样来获取这些宝贝资料吧。
深度学习书单获取步骤：
（1）首先，关注新智元公众号（ID：AI_era）
（2）其次，进入新智元公众号，在新智元公众号处回复关键字 160215
（3）回复关键字后，会出现网址链接，打开链接，即可看到书单中上述各个板块的内容，若想查看板块中具体的一个书目，选中蓝色的标题，右击选择“打开超链接”即可看到书中具体的内容。
你想成为深度学习领域的小能手吗？想真正地了解“深度学习”、“机器学习”、“人工智能”这些科技热词到底是怎么回事吗?答案就从关注新智元，从获取这份深度学习的书单开始寻找吧！！！
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