深度學習（Deep Learning）資料大全（不斷更新）

Deep Learning（深度學習）學習筆記（不斷更新）：

Deep Learning（深度學習）學習筆記之系列（一）

 

深度學習（Deep Learning）資料（不斷更新）：新增數據集，微信公衆號寫的更全些

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深度學習（Deep Learning）資料大全（不斷更新）

 

相關Paper（不斷更新）

筆者先從多個渠道整理了幾篇，後續邊看邊更新。

一、Densely Connected Convolutional Networks

二、Learning From Simulated and Unsupervised Images through Adversarial Training

三、Annotating Object Instance with a Polygon-RNN

四、YOLO9000: Better, Faster, Stronger

五、Computational Imaging on the Electric Grid

六、Object retrieval with large vocabularies and fast spatial matching

七、Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning

八、Pointing the Unknown Words

九、LightRNN Memory and Computation-Efficient Recurrent Neural Network

十、Language Modeling with Gated Convolutional Networks

十一、Recurrent neural network based language model

十二、Extensions of Recurrent Neural Network Language Model

1三、A guide to recurrent neural networks and backpropagation

1四、Training Recurrent Neural Networks

1五、Recurrent Neural Networks for Language Understanding

1六、Empirical Evaluation and Combination of Advanced Language Modeling Techniques

1七、Speech Recognition with Deep Recurrent Neural Networks

1八、A fast learning algorithm for deep belief nets

1九、Large Scale Distributed Deep Networks

20、Context Dependent Pretrained Deep Neural Networks fo Large Vocabulary Speech Recognition

2一、An Empirical Study of Learning Rates in Deep Neural Networks for Speech Recognition

2二、Deep Neural Networks for Acoustic Modeling in Speech Recognition

2三、Deep Belief Networks Using Discriminative Features for Phone Recognition

2四、Improving Deep Neural Networks For LVCSR using Rectified Linear Units and Dropout

2五、Improved feature processing for Deep Neural Networks

2六、Exploiting Sparseness in Deep Neural Networks fo Large Vocabulary Speech Recognition

2七、Learning Features from Music Audio with Deep Belief Networks

2八、Making Deep Belief Networks Effective for Large Vocabulary Continuous Speech Recognition

2九、Robust Visual Recognition Using Multilayer Generative Neural Networks 

30、Deep Convolutional Network Cascade for Facial Point Detection

3一、ImageNet Classification with Deep Convolutional Neural Networks

3二、Gradient-Based Learning Applied to Document Recognition

3三、Stochastic Pooling for Regularization of Deep Convolutional Neural Networks

3四、Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis

3五、Multi-GPU Training of ConvNets

3六、Deep Learning For Signal And Information Processing

3七、Deep Convex Net: A Scalable Architecture for Speech Pattern Classification

3八、Improving Wideband Speech Recognition using Mixed-Bandwidth Training Data in CD-DNN-HMM

3九、On Rectified Linear Units for Speech Processing

更新中。。。

 

相關書籍（不斷更新）

筆者剛着手學習，非大牛，不敢說「推薦」書籍，僅羅列所看的。

一、Deep Learning，出自Goodfellow、Bengio 和 Courville 三位大牛之手，筆者剛開始看，後續再對書籍做評論

若是須要《Deep Learning》中文電子版書籍，請後臺回覆「深度學習」獲取

更新中。。。

 

數據集（不斷更新）：

 1、圖像數據集

1.MNIST:https://datahack.analyticsvidh ... gits/

MNIST是最受歡迎的深度學習數據集之一，這是一個手寫數字數據集，包含一組60,000個示例的訓練集和一個包含10,000個示例的測試集。這是一個很好的數據庫，用於在實際數據中嘗試學習技術和深度識別模式，同時能夠在數據預處理中花費最少的時間和精力。

•大小： 50 MB
•記錄數量： 70,000張圖片被分紅了10個組。
•SOTA： Capsules之間的動態路由

https://arxiv.org/pdf/1710.09829.pdf

2.MS-COCO:http://cocodataset.org/#home

COCO是一個大型的、豐富的物體檢測，分割和字幕數據集。它有幾個特色：

•對象分割；
•在上下文中可識別；
•超像素分割；
•330K圖像（> 200K標記）；
•150萬個對象實例；
•80個對象類別；
•91個類別；
•每張圖片5個字幕；
•有關鍵點的250,000人；
•大小：25 GB（壓縮）
•記錄數量： 330K圖像、80個對象類別、每幅圖像有5個標籤、25萬個關鍵點。
•SOTA：Mask R-CNN：https://arxiv.org/pdf/1703.06870.pdf

3.ImageNet：http://www.image-net.org/

ImageNet是根據WordNet層次結構組織的圖像數據集。WordNet包含大約100,000個單詞，ImageNet平均提供了大約1000個圖像來講明每一個單詞。

大小：150GB

記錄數量：總圖像是大約是1,500,000，每一個都有多個邊界框和相應的類標籤。

SOTA：深度神經網絡的聚合殘差變換。

https://arxiv.org/pdf/1611.05431.pdf

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