Deep Learning（深度学习）学习笔记（不断更新）：
深度学习（Deep Learning）资料（不断更新）：新增数据集，微信公众号写的更全些
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相关Paper（不断更新）
笔者先从多个渠道整理了几篇，后续边看边更新。
1、Densely Connected Convolutional Networks
2、Learning From Simulated and Unsupervised Images through Adversarial Training
3、Annotating Object Instance with a Polygon-RNN
4、YOLO9000: Better, Faster, Stronger
5、Computational Imaging on the Electric Grid
6、Object retrieval with large vocabularies and fast spatial matching
7、Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
8、Pointing the Unknown Words
9、LightRNN Memory and Computation-Efficient Recurrent Neural Network
10、Language Modeling with Gated Convolutional Networks
11、Recurrent neural network based language model
12、Extensions of Recurrent Neural Network Language Model
13、A guide to recurrent neural networks and backpropagation
14、Training Recurrent Neural Networks
15、Recurrent Neural Networks for Language Understanding
16、Empirical Evaluation and Combination of Advanced Language Modeling Techniques
17、Speech Recognition with Deep Recurrent Neural Networks
18、A fast learning algorithm for deep belief nets
19、Large Scale Distributed Deep Networks
20、Context Dependent Pretrained Deep Neural Networks fo Large Vocabulary Speech Recognition
21、An Empirical Study of Learning Rates in Deep Neural Networks for Speech Recognition
22、Deep Neural Networks for Acoustic Modeling in Speech Recognition
23、Deep Belief Networks Using Discriminative Features for Phone Recognition
24、Improving Deep Neural Networks For LVCSR using Rectified Linear Units and Dropout
25、Improved feature processing for Deep Neural Networks
26、Exploiting Sparseness in Deep Neural Networks fo Large Vocabulary Speech Recognition
27、Learning Features from Music Audio with Deep Belief Networks
28、Making Deep Belief Networks Effective for Large Vocabulary Continuous Speech Recognition
29、Robust Visual Recognition Using Multilayer Generative Neural Networks
30、Deep Convolutional Network Cascade for Facial Point Detection
31、ImageNet Classification with Deep Convolutional Neural Networks
32、Gradient-Based Learning Applied to Document Recognition
33、Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
34、Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
35、Multi-GPU Training of ConvNets
36、Deep Learning For Signal And Information Processing
37、Deep Convex Net: A Scalable Architecture for Speech Pattern Classification
38、Improving Wideband Speech Recognition using Mixed-Bandwidth Training Data in CD-DNN-HMM
39、On Rectified Linear Units for Speech Processing
更新中。。。
相关书籍（不断更新）
笔者刚着手学习，非大牛，不敢说“推荐”书籍，仅罗列所看的。
1、Deep Learning，出自Goodfellow、Bengio 和 Courville 三位大牛之手，笔者刚开始看，后续再对书籍作评论
如果需要《Deep Learning》中文电子版书籍，请后台回复“深度学习”获取
更新中。。。
数据集（不断更新）：
一、图像数据集
1.MNIST:https://datahack.analyticsvidh … gits/
MNIST是最受欢迎的深度学习数据集之一，这是一个手写数字数据集，包含一组60,000个示例的训练集和一个包含10,000个示例的测试集。这是一个很好的数据库，用于在实际数据中尝试学习技术和深度识别模式，同时可以在数据预处理中花费最少的时间和精力。
•大小： 50 MB
•记录数量： 70,000张图片被分成了10个组。
•SOTA： Capsules之间的动态路由
https://arxiv.org/pdf/1710.09829.pdf
2.MS-COCO:http://cocodataset.org/#home
COCO是一个大型的、丰富的物体检测，分割和字幕数据集。它有几个特点：
•对象分割；
•在上下文中可识别；
•超像素分割；
•330K图像（> 200K标记）；
•150万个对象实例；
•80个对象类别；
•91个类别；
•每张图片5个字幕；
•有关键点的250,000人；
•大小：25 GB（压缩）
•记录数量： 330K图像、80个对象类别、每幅图像有5个标签、25万个关键点。
•SOTA：Mask R-CNN：https://arxiv.org/pdf/1703.06870.pdf
3.ImageNet：http://www.image-net.org/
ImageNet是根据WordNet层次结构组织的图像数据集。WordNet包含大约100,000个单词，ImageNet平均提供了大约1000个图像来说明每个单词。
大小：150GB
记录数量：总图像是大约是1,500,000，每个都有多个边界框和相应的类标签。
SOTA：深度神经网络的聚合残差变换。
https://arxiv.org/pdf/1611.05431.pdf
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