【深度学习Deep Learning】资料大全

港控/mmm° 2022-05-23 01:19 272阅读 0赞

最近在学深度学习相关的东西，在网上搜集到了一些不错的资料，现在汇总一下：

### Free Online Books ###

1.  [Deep Learning66][] by Yoshua Bengio, Ian Goodfellow and Aaron Courville
2.  [Neural Networks and Deep Learning42][] by Michael Nielsen
3.  [Deep Learning27][] by Microsoft Research
4.  [Deep Learning Tutorial23][] by LISA lab, University of Montreal
5.  [Deep Learning:An MIT Press Book][Deep Learning_An MIT Press Book]

### Courses ###

1.  [Machine Learning10][] by Andrew Ng in Coursera
2.  [Neural Networks for Machine Learning12][] by Geoffrey Hinton in Coursera
3.  [Neural networks class2][] by Hugo Larochelle from Université de Sherbrooke
4.  [Deep Learning Course14][] by CILVR lab @ NYU

### Video and Lectures ###

1.  [How To Create A Mind3][] By Ray Kurzweil - Is a inspiring talk
2.  [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning2][Deep Learning_ Self-Taught Learning and Unsupervised Feature Learning2] By Andrew Ng
3.  [Recent Developments in Deep Learning2][] By Geoff Hinton
4.  [The Unreasonable Effectiveness of Deep Learning][] by Yann LeCun
5.  [Deep Learning of Representations][] by Yoshua bengio
6.  [Principles of Hierarchical Temporal Memory][] by Jeff Hawkins
7.  [Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab1][Machine Learning Discussion Group - Deep Learning w_ Stanford AI Lab1] by Adam Coates
8.  [Making Sense of the World with Deep Learning1][] By Adam Coates
9.  [Demystifying Unsupervised Feature Learning ][Demystifying Unsupervised Feature Learning]By Adam Coates
10. [Visual Perception with Deep Learning3][] By Yann LeCun
11. [Oxford Deep Learning -][Oxford Deep Learning_-][Nando de Freitas ][Oxford Deep Learning_-]：在Oxford开设的深度学习课程，有全套视频

### Papers ###

1.  [ImageNet Classification with Deep Convolutional Neural Networks5][]
2.  [Using Very Deep Autoencoders for Content Based Image Retrieval2][]
3.  [Learning Deep Architectures for AI2][]
4.  [CMU’s list of papers7][CMU_s list of papers7]
5.  [The Learning Machines][] - 一个导论性质的文章，让你大致了解深度学习是什么，用来干什么的。
6.  [Deep Learning][] - (Review Article in Nature, May 2015) 三大神 Yann LeCun, Yoshua Bengio, and Geoffrey Hinton的文章，不解释。
7.  [Growing Pains in Deep Learning][]
8.  [Deep Learning in Neural Networks][] - This technical report provides an overview of deep learning and related techniques with a special focus on developments in recent years. 主要看点是深度学习近两年（2012-2014）的进展情况。

### Tutorials ###

1.  [UFLDL Tutorial 120][]
2.  [Deep Learning Tutorial from Stanford][]:斯坦福的官方Tutorial
3.  [Deep Learning for NLP (without Magic)8][Deep Learning for NLP _without Magic_8]
4.  [A Deep Learning Tutorial: From Perceptrons to Deep Networks5][A Deep Learning Tutorial_ From Perceptrons to Deep Networks5]

### WebSites ###

1.  [deeplearning.net7][]
2.  [deeplearning.stanford.edu6][]
3.  [Forum2][]

### Datasets ###

1.  [MNIST1][] Handwritten digits
2.  [Google House Numbers][] from street view
3.  [CIFAR-10 and CIFAR-1003][]4.  [IMAGENET1][]
4.  [Tiny Images1][] 80 Million tiny images6.  [Flickr Data][] 100 Million Yahoo dataset
5.  [Berkeley Segmentation Dataset 500][]

### Frameworks ###

1.  [Caffe9][]2.  [Torch73][]
2.  [Theano3][]
3.  [cuda-convnet2][]5.  [Ccv1][]
4.  [NuPIC3][]
5.  [DeepLearning4J][]：Java和Scala写的，能在Hadoop和Spark上应用，功能非常强大

### Miscellaneous ###

1.  [Google Plus - Deep Learning Community][]
2.  [Caffe Webinar4][]
3.  [100 Best Github Resources in Github for DL5][]
4.  [Word2Vec3][]
5.  [Caffe DockerFile2][]
6.  [TorontoDeepLEarning convnet][]
7.  [Vision data sets1][]
8.  [Fantastic Torch Tutorial4][] My personal favourite. Also check out [gfx.js1][]

### Github ###

1.  [DeepLearn Toolbox][]
2.  [Caffe Webinar4][]
3.  [100 Best Github Resources in Github for DL5][]
4.  [Word2Vec3][]
5.  [GitHub - Eniac-Xie/PyConvNet: Convolutional Neural Network for python users][GitHub - Eniac-Xie_PyConvNet_ Convolutional Neural Network for python users]  ：一个简单的CNN实现（Python）

### 几个常见应用领域 ###

*  [Video Recognition][] - finding and/or identifying specific items in videos or images
 *  [Self-Driving Cars][] - just like it says, cars that drive without humans
 *  [Natural Language Processing][] - getting computers to understand human vocal languages
 *  [And others][] - A free book chapter on many applications of deep learning

### 几个常用的深度学习代码库 ###

*  [H2O][] - 一个开源的可扩展的库，支持Java, Python, Scala, and R
 *  [Deeplearning4j][] - Java库，整合了Hadoop和Spark
 *  [Caffe][] - Yangqing Jia读研究生的时候开发的，现在还是由Berkeley维护。
 *  [Theano][] - 最流行的Python库

### News ###

*  [Deep Learning News][] - 紧跟深度学习的新闻、研究进展和相关的创业项目。

### CV和NLP方面的应用（左边的链接是论文，右边的是代码） ###

*  Page on Toronto, Home Page of Geoffrey Hinton
 *  Page on Toronto, Home Page of Ruslan R Salakhutdinov
 *  Page on Wustl, ynd/cae.py · GitHub
 *  Page on Icml, https://github.com/lisa-lab/pyle...
 *  Page on Jmlr, pylearn2)
 *  On the difficulty of training recurrent neural networks, trainingRNNs
 *  ImageNet Classification with Deep Convolutional Neural Networks, cuda-convnet - High-performance C++/CUDA implementation of convolutional neural networks - Google Project Hosting
 *  Linguistic Regularities in Continuous Space Word Representations, word2vec - Tool for computing continuous distributed representations of words. - Google Project Hosting

最后一定得推荐这个Github：

[机器学习(Machine Learning)&深度学习(Deep Learning)资料(Chapter 2)][Machine Learning_Deep Learning_Chapter 2]（篇目一是机器学习的资料汇总，篇目二是深度学习的汇总，并且在不断更新中）

>  *  [《Image Scaling using Deep Convolutional Neural Networks》][Image Scaling using Deep Convolutional Neural Networks]
> 
> 介绍:使用卷积神经网络的图像缩放.
> 
>  *  [《Proceedings of The 32nd International Conference on Machine Learning》][Proceedings of The 32nd International Conference on Machine Learning]
> 
> 介绍:ICML2015 论文集,优化4个+稀疏优化1个；强化学习4个，深度学习3个+深度学习计算1个；贝叶斯非参、高斯过程和学习理论3个；还有计算广告和社会选择.[ICML2015 Sessions][].
> 
>  *  [《Image Scaling using Deep Convolutional Neural Networks》][Image Scaling using Deep Convolutional Neural Networks]
> 
> 介绍:使用卷积神经网络的图像缩放.
> 
>  *  [《Microsoft researchers accelerate computer vision accuracy and improve 3D scanning models》][Microsoft researchers accelerate computer vision accuracy and improve 3D scanning models]
> 
> 介绍:，第28届IEEE计算机视觉与模式识别(CVPR)大会在美国波士顿举行。微软研究员们在大会上展示了比以往更快更准的计算机视觉图像分类新模型，并介绍了如何使用Kinect等传感器实现在动态或低光环境的快速大规模3D扫描技术.
> 
>  *  [《Machine Learning for Humans》][Machine Learning for Humans]
> 
> 介绍:(文本)机器学习可视化分析工具.
> 
>  *  [《A Plethora of Tools for Machine Learning》][A Plethora of Tools for Machine Learning]
> 
> 介绍:机器学习工具包/库的综述/比较.
> 
>  *  [《The art of visualizing visualizations: a best practice guide》][The art of visualizing visualizations_ a best practice guide]
> 
> 介绍:数据可视化最佳实践指南.
> 
>  *  [《MIT Machine Learning for Big Data and Text Processing Class Notes - Day 1》][MIT Machine Learning for Big Data and Text Processing Class Notes - Day 1]
> 
> 介绍:[Day 1][MIT Machine Learning for Big Data and Text Processing Class Notes - Day 1]、[Day 2][]、[Day 3][]、[Day 4][]、[Day 5][].
> 
>  *  [《Getting “deep” about “deep learning”》][Getting _deep_ about _deep learning]
> 
> 介绍:深度学习之“深”——DNN的隐喻分析.
> 
>  *  [《Mixture Density Networks》][Mixture Density Networks]
> 
> 介绍:混合密度网络.
> 
>  *  [《Interview Questions for Data Scientist Positions》][Interview Questions for Data Scientist Positions]
> 
> 介绍:数据科学家职位面试题.
> 
>  *  [《Accurately Measuring Model Prediction Error》][Accurately Measuring Model Prediction Error]
> 
> 介绍:准确评估模型预测误差.
> 
>  *  [《Continually updated Data Science Python Notebooks》][Continually updated Data Science Python Notebooks]
> 
> 介绍:Continually updated Data Science Python Notebooks.
> 
>  *  [《How to share data with a statistician》][How to share data with a statistician]
> 
> 介绍:How to share data with a statistician.
> 
>  *  [《The Eyescream Project NeuralNets dreaming natural images》][The Eyescream Project NeuralNets dreaming natural images]
> 
> 介绍:来自Facebook的图像自动生成.
> 
>  *  [《How to share data with a statistician》][How to share data with a statistician]
> 
> 介绍:How to share data with a statistician.
> 
>  *  [《A Neural Conversational Model》][A Neural Conversational Model]
> 
> 介绍:(Google)神经(感知)会话模型.
> 
>  *  [《The 50 Best Masters in Data Science》][The 50 Best Masters in Data Science]
> 
> 介绍:The 50 Best Masters in Data Science.
> 
>  *  [《NLP常用信息资源》][NLP]
> 
> 介绍:NLP常用信息资源.
> 
>  *  [《Conditional Random Fields as Recurrent Neural Networks》][Conditional Random Fields as Recurrent Neural Networks]
> 
> 介绍:语义图像分割的实况[演示][Link 1],通过深度学习技术和概率图模型的语义图像分割.
> 
>  *  [《Fully Convolutional Networks for Semantic Segmentation》][Fully Convolutional Networks for Semantic Segmentation]
> 
> 介绍:Caffe模型/代码:面向图像语义分割的全卷积网络,[模型代码][Link 2].
> 
>  *  [《Growing Pains for Deep Learning》][Growing Pains for Deep Learning]
> 
> 介绍:深度学习——成长的烦恼.
> 
>  *  [《Clustering Text Data Streams – A Tree based Approach with Ternary Function and Ternary Feature Vector 》][Clustering Text Data Streams _ A Tree based Approach with Ternary Function and Ternary Feature Vector]
> 
> 介绍:基于三元树方法的文本流聚类.
> 
>  *  [《Foundations and Advances in Data Mining》][Foundations and Advances in Data Mining]
> 
> 介绍:Free Ebook:数据挖掘基础及最新进展.
> 
>  *  [《The Deep Learning Revolution: Rethinking Machine Learning Pipelines》][The Deep Learning Revolution_ Rethinking Machine Learning Pipelines]
> 
> 介绍:深度学习革命.
> 
>  *  [《The Definitive Guide to Do Data Science for Good》][The Definitive Guide to Do Data Science for Good]
> 
> 介绍:数据科学(实践)权威指南.
> 
>  *  [《Microsoft Academic Graph》][Microsoft Academic Graph]
> 
> 介绍:37G的微软学术图谱数据集.
> 
>  *  [《Challenges and Opportunities Of Machine Learning In Production》][Challenges and Opportunities Of Machine Learning In Production]
> 
> 介绍:生产环境(产品级)机器学习的机遇与挑战.
> 
>  *  [《Neural Nets for Newbies》][Neural Nets for Newbies]
> 
> 介绍:神经网络入门.
> 
>  *  [《A Nearly-Linear Time Framework for Graph-Structured Sparsity》][A Nearly-Linear Time Framework for Graph-Structured Sparsity]
> 
> 介绍:来自麻省理工的结构化稀疏论文.
> 
>  *  [《Optimal and Adaptive Algorithms for Online Boosting》][Optimal and Adaptive Algorithms for Online Boosting]
> 
> 介绍:来自雅虎的机器学习小组关于在线Boosting的论文 .
> 
>  *  [《Top 20 Python Machine Learning Open Source Projects》][Top 20 Python Machine Learning Open Source Projects]
> 
> 介绍:20个最热门的开源(Python)机器学习项目.
> 
>  *  [《The Parallel C++ Statistical Library for Bayesian Inference: QUESO》][The Parallel C_ Statistical Library for Bayesian Inference_ QUESO]
> 
> 介绍:C++并行贝叶斯推理统计库QUESO,[github code][].
> 
>  *  [《《Deep learning》Yann LeCun, Yoshua Bengio, Geoffrey Hinton (2015) 》][Deep learning_Yann LeCun_ Yoshua Bengio_ Geoffrey Hinton _2015_]
> 
> 介绍:Nature:LeCun/Bengio/Hinton的最新文章《深度学习》,Jürgen Schmidhuber的最新评论文章[《Critique of Paper by "Deep Learning Conspiracy" (Nature 521 p 436)》][Critique of Paper by _Deep Learning Conspiracy_ _Nature 521 p 436].
> 
>  *  [《Palladium》][Palladium]
> 
> 介绍:基于Scikit-Learn的预测分析服务框架Palladium.
> 
>  *  [《Advances in Structured Prediction》][Advances in Structured Prediction]
> 
> 介绍:John Langford和Hal Daume III在ICML2015上关于Learning to Search的教学讲座幻灯片.
> 
>  *  [《100 open source Big Data architecture papers for data professionals》][100 open source Big Data architecture papers for data professionals]
> 
> 介绍:读完这100篇论文 就能成大数据高手,[国内翻译][Link 3].
> 
>  *  [《Social Media & Text Analytics》][Social Media _ Text Analytics]
> 
> 介绍:NLP课程《社交媒体与文本分析》精选阅读列表.
> 
>  *  [《Machine Learning for Developers》][Machine Learning for Developers]
> 
> 介绍:写给开发者的机器学习指南.
> 
>  *  [《Hot news detection using Wikipedia》][Hot news detection using Wikipedia]
> 
> 介绍:基于维基百科的热点新闻发现.
> 
>  *  [《Harvard Intelligent Probabilistic Systems Group》][Harvard Intelligent Probabilistic Systems Group]
> 
> 介绍:(Harvard)HIPS将发布可扩展/自动调参贝叶斯推理神经网络.
> 
>  *  [《An Empirical Exploration of Recurrent Network Architectures》][An Empirical Exploration of Recurrent Network Architectures]
> 
> 介绍:面向上下文感知查询建议的层次递归编解码器.
> 
>  *  [《Efficient Training of LDA on a GPU by Mean-for-Mode Estimation》][Efficient Training of LDA on a GPU by Mean-for-Mode Estimation]
> 
> 介绍:GPU上基于Mean-for-Mode估计的高效LDA训练.
> 
>  *  [《From the Lab to the Factory: Building a Production Machine Learning Infrastructure》][From the Lab to the Factory_ Building a Production Machine Learning Infrastructure]
> 
> 介绍:从实验室到工厂——构建机器学习生产架构.
> 
>  *  [《6 Useful Databases to Dig for Data (and 100 more)》][6 Useful Databases to Dig for Data _and 100 more]
> 
> 介绍:适合做数据挖掘的6个经典数据集(及另外100个列表).
> 
>  *  [《Deep Networks for Computer Vision at Google – ILSVRC2014》][Deep Networks for Computer Vision at Google _ ILSVRC2014]
> 
> 介绍:Google面向机器视觉的深度学习.
> 
>  *  [《How to choose a machine learning API to build predictive apps》][How to choose a machine learning API to build predictive apps]
> 
> 介绍:构建预测类应用时如何选择机器学习API.
> 
>  *  [《Exploring the shapes of stories using Python and sentiment APIs》][Exploring the shapes of stories using Python and sentiment APIs]
> 
> 介绍:Python+情感分析API实现故事情节(曲线)分析.
> 
>  *  [《Movie selection using R》][Movie selection using R]
> 
> 介绍:(R)基于Twitter/情感分析的口碑电影推荐,此外推荐[分类算法的实证比较分析][Link 4].
> 
>  *  [《A Tutorial on Graph-based Semi-Supervised Learning Algorithms for NLP》][A Tutorial on Graph-based Semi-Supervised Learning Algorithms for NLP]
> 
> 介绍:CMU(ACL 2012)(500+页)面向NLP基于图的半监督学习算法.
> 
>  *  [《Arbitrariness of peer review: A Bayesian analysis of the NIPS experiment》][Arbitrariness of peer review_ A Bayesian analysis of the NIPS experiment]
> 
> 介绍:从贝叶斯分析NIPS，看同行评审的意义.
> 
>  *  [《Basics of Computational Reinforcement Learning》][Basics of Computational Reinforcement Learning]
> 
> 介绍:(RLDM 2015)计算强化学习入门.
> 
>  *  [《Deep Reinforcement Learning》][Deep Reinforcement Learning]
> 
> 介绍:David Silver的深度强化学习教程.
> 
>  *  [《On Explainability of Deep Neural Networks》][On Explainability of Deep Neural Networks]
> 
> 介绍:深度神经网络的可解释性.
> 
>  *  [《The Essential Spark Cheat Sheet》][The Essential Spark Cheat Sheet]
> 
> 介绍:Spark快速入门.
> 
>  *  [《Machine Learning for Sports and Real Time Predictions》][Machine Learning for Sports and Real Time Predictions]
> 
> 介绍:TalkingMachines:面向体育/政治和实时预测的机器学习.
> 
>  *  [《CS224W: Social and Information Network Analysis Autumn 2014》][CS224W_ Social and Information Network Analysis Autumn 2014]
> 
> 介绍:Stanford社交网络与信息网络分析课程[资料][Link 5]\+[课设][Link 6]\+[数据][Link 7].
> 
>  *  [《RL Course by David Silver》][RL Course by David Silver]
> 
> 介绍:David Silver(DeeMind)的强化学习课程,[slide][].
> 
>  *  [《Faster deep learning with GPUs and Theano》][Faster deep learning with GPUs and Theano]
> 
> 介绍:基于Theano/GPU的高效深度学习.
> 
>  *  [《Introduction to R Programming》][Introduction to R Programming]
> 
> 介绍:来自微软的.
> 
>  *  [《Golang:Web Server For Performing Sentiment Analysis》][Golang_Web Server For Performing Sentiment Analysis]
> 
> 介绍:(Go)情感分析API服务Sentiment Server.
> 
>  *  [《A Beginner’s Guide to Restricted Boltzmann Machines》][A Beginner_s Guide to Restricted Boltzmann Machines]
> 
> 介绍:受限波尔兹曼机初学者指南.
> 
>  *  [《KDD2015十年最佳论文》][KDD2015]
> 
> 介绍:[Mining and Summarizing Customer Reviews ][Mining and Summarizing Customer Reviews],[Mining High-Speed Data Streams][],[Optimizing Search Engines using Clickthrough Data][].
> 
>  *  [《Nvidia Deep Learning Courses》][Nvidia Deep Learning Courses]
> 
> 介绍:Nvidia深度学习课程.
> 
>  *  [《Deep Learning Summer School 2015》][Deep Learning Summer School 2015]
> 
> 介绍:2015年深度学习暑期课程，推荐[讲师主页][Link 8].
> 
>  *  [《百度深度学习的图像识别进展》][Link 9]
> 
> 介绍:这是一篇关于百度文章[《基于深度学习的图像识别进展：百度的若干实践》][Link 10]的摘要,建议两篇文章结合起来阅读.
> 
>  *  [《Machine Learning Methods in Video Annotation》][Machine Learning Methods in Video Annotation]
> 
> 介绍:视频标注中的机器学习技术.
> 
>  *  [《Training Recurrent Neural Networks》][Training Recurrent Neural Networks]
> 
> 介绍:博士论文:(Ilya Sutskever)RNN训练.
> 
>  *  [《On Explainability of Deep Neural Networks》][On Explainability of Deep Neural Networks]
> 
> 介绍:深度神经网络的灰色区域：可解释性问题，[中文版][Link 11].
> 
>  *  [《Machine Learning Libraries in GoLang by Category》][Machine Learning Libraries in GoLang by Category]
> 
> 介绍:Golang 实现的机器学习库资源汇总.
> 
>  *  [《A Statistical View of Deep Learning》][A Statistical View of Deep Learning]
> 
> 介绍:深度学习的统计分析.
> 
>  *  [《Deep Learning For NLP - Tips And Techniques》][Deep Learning For NLP - Tips And Techniques]
> 
> 介绍:面向NLP的深度学习技术与技巧.
> 
>  *  [《CrowdFlower Competition Scripts: Approaching NLP》][CrowdFlower Competition Scripts_ Approaching NLP]
> 
> 介绍:Kaggle's CrowdFlower竞赛NLP代码集锦.
> 
>  *  [《CS224U: Natural Language Understanding》][CS224U_ Natural Language Understanding]
> 
> 介绍:斯坦福的自然语言理解课程.
> 
>  *  [《Deep Learning and Shallow Learning》][Deep Learning and Shallow Learning]
> 
> 介绍:Deep Learning与Shallow Learning 介绍
> 
>  *  [《A First Encounter with Machine Learning》][A First Encounter with Machine Learning]
> 
> 介绍:这是一本机器学习的电子书,作者[Max Welling][]先生在机器学习教学上面有着丰富的经验,这本书小但精致.
> 
>  *  [《Click Models for Web Search》][Click Models for Web Search]
> 
> 介绍:由荷兰阿姆斯特丹大学 & 谷歌瑞士著.
> 
>  *  [《Hinton CSC321课程/Deep Learning/Notes on CNN/Python/Theano/CUDA/OpenCV/...》][Hinton CSC321_Deep Learning_Notes on CNN_Python_Theano_CUDA_OpenCV_...]
> 
> 介绍:介绍个乐于总结和翻译机器学习和计算机视觉类资料的博客,包含的内容：Hinton的CSC321课程的总结；Deep Learning综述；Notes on CNN的总结；python的原理总结；Theano基础知识和练习总结；CUDA原理和编程；OpenCV一些总结.
> 
>  *  [《Which Algorithm Family Can Answer My Question?》][Which Algorithm Family Can Answer My Question]
> 
> 介绍:针对具体问题(应用场景)如何选择机器学习算法(系列).
> 
>  *  [《Free Data Science Books》][Free Data Science Books]
> 
> 介绍:数据科学免费书分类集合
> 
>  *  [《Tutorial 4: Deep Learning for Speech Generation and Synthesis》][Tutorial 4_ Deep Learning for Speech Generation and Synthesis]
> 
> 介绍:深度学习在语音合成最新进展有哪些？推荐MSRA的Frank Soong老师关于语音合成的深度学习方法的录像和幻灯片与以及谷歌的LSTM-RNN合成[介绍][Link 12],[论文][Link 13]
> 
>  *  [《The Art of Data Science》][The Art of Data Science]
> 
> 介绍:新书(可免费下载):数据科学的艺术
> 
>  *  [《Pattern Recognition and Machine Learning》][Pattern Recognition and Machine Learning]
> 
> 介绍:模式识别与机器学习书籍推荐,本书是微软剑桥研究院大神Bishop所写，算是最为广为认知的机器学习教材之一，内容覆盖全面，难度中上，适合研究生[中文版][Link 14] or [备份][Link 15]
> 
>  *  [《an introduction to visualizing DATA》][an introduction to visualizing DATA]
> 
> 介绍:数据可视化介绍(23页袖珍小册子)
> 
>  *  [《That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using \#petpeeve Tweets ∗》][That_s So Annoying_ A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using _petpeeve Tweets]
> 
> 介绍:这篇论文荣获EMNLP2015的最佳数据/资源奖优秀奖,[标注的推特数据集][Link 16]
> 
>  *  [《26 Things I Learned in the Deep Learning Summer School》][26 Things I Learned in the Deep Learning Summer School]
> 
> 介绍:作者在深度学习的思考.
> 
>  *  [《Data-Visualization Tools & Books》][Data-Visualization Tools _ Books]
> 
> 介绍:数据可视化常用工具软件资源汇总
> 
>  *  [《Machine Learning and Probabilistic Graphical Models Course》][Machine Learning and Probabilistic Graphical Models Course]
> 
> 介绍:Buffalo大学教授Sargur Srihari的“机器学习和概率图模型”的视频课程
> 
>  *  [《Understanding Machine Learning: From Theory to Algorithms》][Understanding Machine Learning_ From Theory to Algorithms]
> 
> 介绍:耶路撒冷希伯来大学教授Shai Shalev-Shwartz和滑铁卢大学教授Shai Ben-David的新书Understanding Machine Learning: From Theory to Algorithms,此书写的比较偏理论，适合对机器学习理论有兴趣的同学选读
> 
>  *  [《Machine Learning Checklist》][Machine Learning Checklist]
> 
> 介绍:机器学习学习清单
> 
>  *  [《NLP界有哪些神级人物？》][NLP 1]
> 
> 介绍:知乎上面的一篇关于NLP界有哪些神级人物？提问。首推Michael Collins 
> 
>  *  [《机器学习温和指南》][Link 17]
> 
> 介绍:机器学习与NLP专家、MonkeyLearn联合创始人&CEO Raúl Garreta面向初学者大体概括使用机器学习过程中的重要概念，应用程序和挑战，旨在让读者能够继续探寻机器学习知识。
> 
>  *  [《Gradient Boosted Regression Trees》][Gradient Boosted Regression Trees]
> 
> 介绍:(IPN)基于Scikit-Learn的GBRT(Gradient Boost Regression Tree)教程，[slide][slide 1]
> 
>  *  [《Apache SINGA : Distributed Deep Learning System》][Apache SINGA _ Distributed Deep Learning System]
> 
> 介绍: 无需做深度学习就能用的分布式深度学习软件.
> 
>  *  [《E-commerce Recommendation with Personalized Promotion》][E-commerce Recommendation with Personalized Promotion]
> 
> 介绍: 在亚马逊数据和众包Mechanical Turk上，实现了来自彩票和拍卖的机制，以收集用户对产品的乐意购买价格（WTP，willingness-to-pay）训练集。 E-commerce Recommendation with Personalized Promotion \[Zhao,RecSys15\] 回归模型预测未知WTP，提升卖家利润和消费者满意度
> 
>  *  [《Scalable Machine Learning》][Scalable Machine Learning]
> 
> 介绍:来自伯克利分校的大规模机器学习.
> 
>  *  [《机器学习资料大汇总》][Link 18]
> 
> 介绍:来自52ml的机器学习资料大汇总.
> 
>  *  [《Automatic Summarization》][Automatic Summarization]
> 
> 介绍:这本书的作[者McKeown][McKeown]是2013年世界首个数据科学院（位于哥伦比亚大学）主任，她亦是ACL、AAAI和ACM Fellow .
> 
>  *  [《Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing》][Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing]
> 
> 介绍:EMNLP-15文本摘要若干.
> 
>  *  [《Recommender Systems (Machine Learning Summer School 2014 @ CMU)》][Recommender Systems _Machine Learning Summer School 2014 _ CMU]
> 
> 介绍:来自Netflix的Xavier Amatriain在Summer School 2014 @ CMU上长达4小时的报告，共248页，是对推荐系统发展的一次全面综述，其中还包括Netflix在个性化推荐方面的一些经验介绍.
> 
>  *  [《BigData Stream Mining》][BigData Stream Mining]
> 
> 介绍:(ECML PKDD 2015)大数据流挖掘教程,此外推荐[ECML PKDD 2015 Tutorial列表][ECML PKDD 2015 Tutorial].
> 
>  *  [《Deep learning on Spark with Keras》][Deep learning on Spark with Keras]
> 
> 介绍:Spark上的[Keras][]深度学习框架Elephas.
> 
>  *  [《Prof. Surya Ganguli - The statistical physics of deep learning》][Prof. Surya Ganguli - The statistical physics of deep learning]
> 
> 介绍:Surya Ganguli深度学习统计物理学.
> 
>  *  [《(系统/算法/机器学习/深度学习/图模型/优化/...)在线视频课程列表》][...]
> 
> 介绍:(系统/算法/机器学习/深度学习/图模型/优化/...)在线视频课程列表.
> 
>  *  [《Introduction to Topic Modeling in Python》][Introduction to Topic Modeling in Python]
> 
> 介绍:(PyTexas 2015)Python主题建模.
> 
>  *  [《Large Scale Distributed Deep Learning on Hadoop Clusters》][Large Scale Distributed Deep Learning on Hadoop Clusters]
> 
> 介绍:Hadoop集群上的大规模分布式机器学习.
> 
>  *  [《Top Deep Learning Employers Based On LinkedIn Data》][Top Deep Learning Employers Based On LinkedIn Data]
> 
> 介绍:基于LinkedIn数据得出的深度学习热门"东家"排行.
> 
>  *  [《Neural Net in C++ Tutorial》][Neural Net in C_ Tutorial]
> 
> 介绍:(c++)神经网络手把手实现教程.
> 
>  *  [《Large-scale CelebFaces Attributes (CelebA) Dataset》][Large-scale CelebFaces Attributes _CelebA_ Dataset]
> 
> 介绍:香港中文大学汤晓鸥教授实验室公布的大型人脸识别数据集： Large-scale CelebFaces Attributes (CelebA) Dataset 10K 名人，202K 脸部图像，每个图像40余标注属性.
> 
>  *  [《Unsupervised Feature Learning in Computer Vision》][Unsupervised Feature Learning in Computer Vision]
> 
> 介绍:面向机器视觉的无监督特征学习,[Ross Goroshin's webpage][Ross Goroshin_s webpage].
> 
>  *  [《Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks》][Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks]
> 
> 介绍:谷歌研究院Samy Bengio等人最近写的RNN的Scheduled Sampling训练方法论文.
> 
>  *  [《Essential Machine Learning Algorithms in a nutshell》][Essential Machine Learning Algorithms in a nutshell]
> 
> 介绍:机器学习基本算法简要入门.
> 
>  *  [《A Huge List of Machine Learning And Statistics Repositories》][A Huge List of Machine Learning And Statistics Repositories]
> 
> 介绍:Github机器学习/数学/统计/可视化/深度学习相关项目大列表.
> 
>  *  [《Information Processing and Learning》][Information Processing and Learning]
> 
> 介绍:CMU的信息论课程.
> 
>  *  [《Scheduled sampling for sequence prediction with recurrent neural networks》][Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks]
> 
> 介绍:谷歌研究院[Samy Bengio][]等人最近写的RNN的Scheduled Sampling训练方法论文.
> 
>  *  [《基于Hadoop集群的大规模分布式深度学习》][Hadoop]
> 
> 介绍:基于Hadoop集群的大规模分布式深度学习.
> 
>  *  [《Learning both Weights and Connections for Efficient Neural Networks习》][Learning both Weights and Connections for Efficient Neural Networks]
> 
> 介绍:来自斯坦福大学及NVIDIA的工作，很实在很实用。采用裁剪网络连接及重训练方法，可大幅度减少CNN模型参数。针对AlexNet、VGG等模型及ImageNet数据，不损失识别精度情况下，模型参数可大幅度减少9-13倍.
> 
>  *  [《Apache Singa --A General Distributed Deep Learning Platform》][Apache SINGA _ Distributed Deep Learning System]
> 
> 介绍:无需做深度学习就能用的分布式深度学习软件,[github][].
> 
>  *  [《24 Ultimate Data Scientists To Follow in the World Today》][24 Ultimate Data Scientists To Follow in the World Today]
> 
> 介绍:当今世界最NB的25位大数据科学家,通过他们的名字然后放在google中搜索肯定能找到很多很棒的资源[译文][Link 19].
> 
>  *  [《Deep Learning for NLP - Lecture October 2015》][Deep Learning for NLP - Lecture October 2015]
> 
> 介绍:Nils Reimers面向NLP的深度学习(Theano/Lasagne)系列教程.
> 
>  *  [《Connection between probability theory and real analysis》][Connection between probability theory and real analysis]
> 
> 介绍:主讲人是[陶哲轩][Connection between probability theory and real analysis],资料[Probability: Theory and Examples][Probability_ Theory and Examples],[笔记][Link 20].
> 
>  *  [《Data Science Learning Resources》][Data Science Learning Resources]
> 
> 介绍:数据科学(学习)资源列表.
> 
>  *  [《8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset》][8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset]
> 
> 介绍:应对非均衡数据集分类问题的八大策略.
> 
>  *  [《Top 20 Data Science MOOCs》][Top 20 Data Science MOOCs]
> 
> 介绍:重点推荐的20个数据科学相关课程.
> 
>  *  [《Recurrent Neural Networks》][Recurrent Neural Networks]
> 
> 介绍:递归神经网络.
> 
>  *  [《Histograms of Oriented Gradients》][Histograms of Oriented Gradients]
> 
> 介绍:(HOG)学习笔记.
> 
>  *  [《Computational modelling courses》][Computational modelling courses]
> 
> 介绍:计算建模/计算神经学课程汇总.
> 
>  *  [《How We Use Deep Learning to Classify Business Photos at Yelp》][How We Use Deep Learning to Classify Business Photos at Yelp]
> 
> 介绍:(Yelp)基于深度学习的商业图片分类.
> 
>  *  [《Neural Networks and Deep Learning》][Neural Networks and Deep Learning42]
> 
> 介绍:免费在线书《Neural Networks and Deep Learning》神经网络与深度学习。目前提供了前四章的草稿，[第一章][Link 21]通过手写数字识别的例子介绍NN，第二章讲反向传播算法，第三章讲反向传播算法的优化，第四章讲NN为什么能拟合任意函数。大量python代码例子和交互动画，生动有趣.[中文版][Link 22]
> 
>  *  [《Books to Read if You Might Be Interested in Data Science》][Books to Read if You Might Be Interested in Data Science]
> 
> 介绍:数据科学大咖荐书(入门).
> 
>  *  [《Deep Learning for NLP resources》][Deep Learning for NLP resources]
> 
> 介绍:NLP 深度学习资源列表.
> 
>  *  [《GitXiv》][GitXiv]
> 
> 介绍:很多arXiv上面知名论文可以在这个网站找到github的项目链接.
> 
>  *  [《Learning Multi-Domain Convolutional Neural Networks for Visual Tracking》][Learning Multi-Domain Convolutional Neural Networks for Visual Tracking]
> 
> 介绍:深度学习在视觉跟踪的探索.
> 
>  *  [《Beginners Guide: Apache Spark Machine Learning Scenario With A Large Input Dataset》][Beginners Guide_ Apache Spark Machine Learning Scenario With A Large Input Dataset]
> 
> 介绍:Spark机器学习入门实例——大数据集(30+g)二分类.
> 
>  *  [《Semantic Scholar》][Semantic Scholar]
> 
> 介绍:保罗艾伦人工智能实验室表示，Google Scholar是十年前的产物，他们现在想要做进一步的提高。于是推出了全新的，专门针对科学家设计的学术搜索引擎Semantic Scholar.
> 
>  *  [《Semi-Supervised Learning》][Semi-Supervised Learning]
> 
> 介绍:半监督学习,Chapelle.篇篇都是经典,作者包括Vapnik,Bengio,Lafferty,Jordan.此外推荐[Xiaojin (Jerry) Zhu][Xiaojin _Jerry_ Zhu]编写的[Introduction to Semi-Supervised Learning][].
> 
> 介绍:Spark机器学习入门实例——大数据集(30+g)二分类.
> 
>  *  [《Free Resources for Beginners on Deep Learning and Neural Network》][Free Resources for Beginners on Deep Learning and Neural Network]
> 
> 介绍:为入门者准备的深度学习与神经网络免费资源.
> 
>  *  [《TensorFlow is an Open Source Software Library for Machine Intelligence》][TensorFlow is an Open Source Software Library for Machine Intelligence]
> 
> 介绍:Google 开源最新机器学习系统 TensorFlow,此外提供TensorFlow白皮书[white paper of tensorflow 2015][].[hacker news][],[Google大牛解读TensorFlow][Google_TensorFlow]
> 
>  *  [《Veles:Distributed machine learning platform》][Veles_Distributed machine learning platform]
> 
> 介绍:三星开源的快速深度学习应用程序开发分布式平台.
> 
>  *  [《DMTK:Microsoft Distributed Machine Learning Tookit 》][DMTK_Microsoft Distributed Machine Learning Tookit]
> 
> 介绍:分布式机器学习工具包.
> 
>  *  [《Semantics Approach to Big Data and Event Processing》][Semantics Approach to Big Data and Event Processing]
> 
> 介绍:语义大数据——大数据/事件处理的语义方法.
> 
>  *  [《LSTM(Long Short Term Memory)和RNN(Recurrent)学习教程》][LSTM_Long Short Term Memory_RNN_Recurrent]
> 
> 介绍:LSTM(Long Short Term Memory)和RNN(Recurrent)学习教程.
> 
>  *  [《Marvin：A minimalist GPU-only N-dimensional ConvNet framework》][Marvin_A minimalist GPU-only N-dimensional ConvNet framework]
> 
> 介绍:Princeton Vision Group的深度学习库开源.
> 
>  *  [《Ufora is a compiled, automatically parallel subset of python for data science and numerical computing》][Ufora is a compiled_ automatically parallel subset of python for data science and numerical computing]
> 
> 介绍:基于AWS的自动分布式科学计算库Ufora,[Why I Open Sourced Five Years of Work][].
> 
>  *  [《Deep Learning and Deep Data Science - PyCon SE 2015》][Deep Learning and Deep Data Science - PyCon SE 2015]
> 
> 介绍:(PyCon SE 2015)深度学习与深度数据科学.
> 
>  *  [《Zhi-Hua Zhou Papers》][Zhi-Hua Zhou Papers]
> 
> 介绍:推荐南京大学机器学习与数据挖掘研究所所长——周志华教授的Google学术主页.
> 
>  *  [《Advanced Linear Models for Data Science》][Advanced Linear Models for Data Science]
> 
> 介绍:免费书:面向数据科学的高级线性模型.
> 
>  *  [《Net2Net: Accelerating Learning via Knowledge Transfer》][Net2Net_ Accelerating Learning via Knowledge Transfer]
> 
> 介绍:基于知识迁移的神经网络高效训练Net2Net.
> 
>  *  [《徐亦达机器学习课程 Variational Inference》][Variational Inference]
> 
> 介绍:徐亦达机器学习课程 Variational Inference.
> 
>  *  [《Learning the Architecture of Deep Neural Networks》][Learning the Architecture of Deep Neural Networks]
> 
> 介绍:深度神经网络结构学习.
> 
>  *  [《Multimodal Deep Learning》][Multimodal Deep Learning]
> 
> 介绍:来自斯坦福大学的Multimodal Deep Learning papers.
> 
>  *  [《深度学习简析,TensorFlow,Torch,Theano,Mxnet》][TensorFlow_Torch_Theano_Mxnet]
> 
> 介绍:深度学习简析,TensorFlow,Torch,Theano,[Mxnet][].
> 
>  *  [《"Notes Essays —CS183C: Technology-enabled Blitzscaling — Stanford University》][Notes Essays _CS183C_ Technology-enabled Blitzscaling_Stanford University]
> 
> 介绍:这个专栏是一个stanford学生做的CS183c课程的一个note，该课程是由Reid Hoffman等互联网boss级人物开设的，每节课请一位巨头公司的相关负责人来做访谈，讲述该公司是怎么scale的。最新两期分别请到了雅虎的梅姐和airbnb创始人Brian Chesky。.
> 
>  *  [《Natural Language Understanding with Distributed Representation》][Natural Language Understanding with Distributed Representation]
> 
> 介绍:基于分布式表示的自然语言理解(100+页),[论文][Link 23].
> 
>  *  [《Recommender Systems Handbook》][Recommender Systems Handbook]
> 
> 介绍:推荐系统手册.
> 
>  *  [《Understanding LSTM Networks》][Understanding LSTM Networks]
> 
> 介绍:理解LSTM网络[翻译][Link 24].
> 
>  *  [《Machine Learning at Quora》][Machine Learning at Quora]
> 
> 介绍:机器学习在quora中的应用.
> 
>  *  [《On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models》][On Learning to Think_ Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models]
> 
> 介绍:思维学习——RL+RNN算法信息论.
> 
>  *  [《The 5 Ways Data Scientists Keep Learning After College》][The 5 Ways Data Scientists Keep Learning After College]
> 
> 介绍:数据科学家毕业后继续学习的5种方式.
> 
>  *  [《Deep Learning in Neural Networks: An Overview》][Deep Learning in Neural Networks_ An Overview]
> 
> 介绍:深度学习在神经网络的应用.
> 
>  *  [《Contextual Learning》][Contextual Learning]
> 
> 介绍:上下文学习,[代码][Link 25].
> 
>  *  [《Machine Learning For Complete Beginners》][Machine Learning For Complete Beginners]
> 
> 介绍:机器学习零基础入门,[代码][Link 26].
> 
>  *  [《2015年中国计算机学会（CCF）优秀博士学位论文》][2015_CCF]
> 
> 介绍:2015年度CCF优秀博士学位论文奖论文列表.
> 
>  *  [《Learning to Hash Paper, Code and Dataset》][Learning to Hash Paper_ Code and Dataset]
> 
> 介绍:Learning to Hash Paper, Code and Dataset.
> 
>  *  [《Neural networks with Theano and Lasagne》][Neural networks with Theano and Lasagne]
> 
> 介绍:(PyData2015)基于Theano/Lasagne的CNN/RNN教程,[github][github 1].
> 
>  *  [《神经网络与深度学习讲义》][Link 27]
> 
> 介绍:复旦大学[邱锡鹏][Link 28]老师编写的神经网络与深度学习讲义,[ppt][].
> 
>  *  [《Microsoft Open Sources Distributed Machine Learning Toolkit》][Microsoft Open Sources Distributed Machine Learning Toolkit]
> 
> 介绍:微软亚洲研究院开源分布式机器学习工具包.
> 
>  *  [《语音识别的技术原理是什么？》][Link 29]
> 
> 介绍:语音识别的技术原理浅析
> 
>  *  [《Michael I. Jordan》][Michael I. Jordan]
> 
> 介绍:迈克尔·I.乔丹的主页.根据主页可以找到很多资源。迈克尔·I.乔丹是知名的计算机科学和统计学学者，主要研究机器学习和人工智能。他的重要贡献包括指出了机器学习与统计学之间的联系，并推动机器学习界广泛认识到贝叶斯网络的重要性。
> 
>  *  [《Geoff Hinton》][Geoff Hinton]
> 
> 介绍:杰弗里·埃弗里斯特·辛顿 FRS是一位英国出生的计算机学家和心理学家，以其在神经网络方面的贡献闻名。辛顿是反向传播算法和对比散度算法的发明人之一，也是深度学习的积极推动者.通过他的主页可以发掘到很多Paper以及优秀学生的paper,此外推荐他的学生[Yann Lecun][]主页
> 
>  *  [《Yoshua Bengio》][Yoshua Bengio]
> 
> 介绍:Yoshua Bengio是机器学习方向的牛人,如果你不知道可以阅读[对话机器学习大神Yoshua Bengio（上）][Yoshua Bengio 1],[对话机器学习大神Yoshua Bengio（下）][Yoshua Bengio 2]
> 
>  *  [《Large Scale Deep Learning within google》][Large Scale Deep Learning within google]
> 
> 介绍:google大规模深度学习应用演进
> 
>  *  [《Deep Learning: An MIT Press Book in Preparation》][Deep Learning_ An MIT Press Book in Preparation]
> 
> 介绍:MIT出版的深度学习电子书,公开电子书
> 
>  *  [《A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction》][A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction]
> 
> 介绍:深度卷积神经网络(CNN)提取特征的数学理论
> 
>  *  [《Microsoft Research Asia：Kaiming He》][Microsoft Research Asia_Kaiming He]
> 
> 介绍:推荐微软亚洲研究院何恺明主页
> 
>  *  [《Speech and Language Processing (3rd ed. draft)》][Speech and Language Processing _3rd ed. draft]
> 
> 介绍:《语音与语言处理》第三版(草稿)
> 
>  *  [《LSA 311: Computational Lexical Semantics - Summer 2015》][LSA 311_ Computational Lexical Semantics - Summer 2015]
> 
> 介绍:Stanford新课"计算词汇语义学"
> 
>  *  [《上海交大张志华老师的统计机器学习与机器学习导论视频》][Link 30]
> 
> 介绍:上海交大张志华老师的统计机器学习与机器学习导论视频[链接:][Link 31]密码: r9ak .[概率基础][Link 32]
> 
>  *  [《Computational Linguistics and Deep Learning》][Computational Linguistics and Deep Learning]
> 
> 介绍:computational linguistics and deep learning[视频][Link 33],推荐[Deep Learning: An Introduction from the NLP Perspective][Deep Learning_ An Introduction from the NLP Perspective]
> 
>  *  [《Black Hat USA 2015 - Deep Learning On Disassembly》][Black Hat USA 2015 - Deep Learning On Disassembly]
> 
> 介绍:(BlackHat2015)深度学习应用之流量鉴别(协议鉴别/异常检测),\[slide\])([https://www.blackhat.com/docs/us-15/materials/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification.pdf),\[material\](https://www.blackhat.com/docs/us-15/materials/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification-wp.pdf][https_www.blackhat.com_docs_us-15_materials_us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification.pdf_material_https_www.blackhat.com_docs_us-15_materials_us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification-wp.pdf])
> 
>  *  [《LibRec：A Java Library for Recommender Systems》][LibRec_A Java Library for Recommender Systems]
> 
> 介绍:一个推荐系统的Java库
> 
>  *  [《Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection》][Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection]
> 
> 介绍:多中心图的谱分解及其在网络入侵检测中的应用(MC-GPCA&MC-GDL)
> 
>  *  [《Computational Statistics in Python》][Computational Statistics in Python]
> 
> 介绍:用Python学计算统计学
> 
>  *  [《New open-source Machine Learning Framework written in Java》][New open-source Machine Learning Framework written in Java]
> 
> 介绍:datumbox-framework——Java的开源机器学习框架，该框架重点是提供大量的机器学习算法和统计检验，并能够处理中小规模的数据集
> 
>  *  [《Awesome Recurrent Neural Networks》][Awesome Recurrent Neural Networks]
> 
> 介绍:递归神经网络awesome系列,涵盖了书籍,项目,paper等
> 
>  *  [《Pedro Domingos》][Pedro Domingos]
> 
> 介绍:Pedro Domingos是华盛顿大学的教授,主要研究方向是机器学习与数据挖掘.在2015年的ACM webinar会议,曾发表了关于[盘点机器学习领域的五大流派][Link 34]主题演讲.他的个人主页拥有很多相关研究的paper以及他的教授课程.
> 
>  *  [《Video resources for machine learning》][Video resources for machine learning]
> 
> 介绍:机器学习视频集锦
> 
>  *  [《Deep Machine Learning libraries and frameworks》][Deep Machine Learning libraries and frameworks]
> 
> 介绍:深度机器学习库与框架
> 
>  *  [《大数据/数据挖掘/推荐系统/机器学习相关资源》][Link 35]
> 
> 介绍:这篇文章内的推荐系统资源很丰富,作者很有心,摘录了《推荐系统实战》内引用的论文.
> 
>  *  [《Bayesian Methods in Astronomy: Hands-on Statistics》][Bayesian Methods in Astronomy_ Hands-on Statistics]
> 
> 介绍:(天文学)贝叶斯方法/MCMC教程——统计实战
> 
>  *  [《Statistical Learning with Sparsity: The Lasso and Generalizations》][Statistical Learning with Sparsity_ The Lasso and Generalizations]
> 
> 介绍:免费书:统计稀疏学习,作者[Trevor Hastie][]与[Rob Tibshirani][]都是斯坦福大学的教授,Trevor Hastie更是在统计学学习上建树很多
> 
>  *  [《The Evolution of Distributed Programming in R》][The Evolution of Distributed Programming in R]
> 
> 介绍:R分布式计算的进化,此外推荐[(R)气候变化可视化][R],[(R)马尔可夫链入门][R 1]
> 
>  *  [《neon workshop at Startup.ML: Sentiment Analysis and Deep Reinforcement Learning》][neon workshop at Startup.ML_ Sentiment Analysis and Deep Reinforcement Learning]
> 
> 介绍:Nervana Systems在[Startup.ML][]的主题研讨会——情感分析与深度强化学习
> 
>  *  [《Understanding Convolution in Deep Learning》][Understanding Convolution in Deep Learning]
> 
> 介绍:深度学习卷积概念详解.
> 
>  *  [《Python libraries for building recommender systems》][Python libraries for building recommender systems]
> 
> 介绍:Python推荐系统开发库汇总.
> 
>  *  [《Neural networks class - Université de Sherbrooke》][Neural networks class - Universit_ de Sherbrooke]
> 
> 介绍:超棒的神经网络课程，深入浅出介绍深度学习，由Hugo Larochelle（Yoshua Bengio的博士生，Geoffrey Hinton之前的博士后）主讲，强烈推荐.
> 
>  *  [《CS231n: Convolutional Neural Networks for Visual Recognition》][CS231n_ Convolutional Neural Networks for Visual Recognition]
> 
> 介绍:斯坦福新课程,面向视觉识别的卷积神经网络(Fei-Fei Li & Andrej Karpathy),[slides+video][slides_video],[homework][].
> 
>  *  [《NIPS 2015 Deep Learning Symposium Part I》][NIPS 2015 Deep Learning Symposium Part I]
> 
> 介绍:NIPS 2015会议总结第一部分,[第二部分][Link 36].
> 
>  *  [《python机器学习入门资料梳理》][python]
> 
> 介绍:python机器学习入门资料梳理.
> 
>  *  [《Reading Text in the Wild with Convolutional Neural Networks》][Reading Text in the Wild with Convolutional Neural Networks]
> 
> 介绍:牛津大学著名视觉几何组VGG在IJCV16年首卷首期: Reading Text in the Wild with Convolutional Neural Networks,Jaderberg。这篇期刊文章融合了之前两篇会议(ECCV14,NIPS14ws)，定位和识别图片中的文本(叫text spotting)。 端到端系统: 检测Region + 识别CNN。论文、数据和代码.
> 
>  *  [《Yet Another Computer Vision Index To Datasets (YACVID)》][Yet Another Computer Vision Index To Datasets _YACVID]
> 
> 介绍:计算机视觉的一个较大的数据集索引, 包含387个标签，共收录了314个数据集合，点击标签云就可以找到自己需要的库了.
> 
>  *  [《Why SLAM Matters, The Future of Real-Time SLAM, and Deep Learning vs SLAM》][Why SLAM Matters_ The Future of Real-Time SLAM_ and Deep Learning vs SLAM]
> 
> 介绍:Tombone 对 ICCV SLAM workshop 的总结： the future of SLAM, SLAM vs deep learning 重点介绍了 monoSLAM 和 LSD-SLAM，而且讨论了 feature-based 和 feature-free method 的长短。在全民deep learning做visual perception的时候，再来读读CV中的 geometry.
> 
>  *  [《Python based Deep Learning Framework by Nervana™》][Python based Deep Learning Framework by Nervana]
> 
> 介绍:Nervana Systems的开源深度学习框架neon发布.
> 
>  *  [《mageNet and MS COCO Visual Recognition Challenges video and slider》][mageNet and MS COCO Visual Recognition Challenges video and slider]
> 
> 介绍:ICCV 2015的ImageNet比赛以及MS COCO竞赛联合研讨会的幻灯片和视频.
> 
>  *  [《An Introduction to Machine Learning with Python》][An Introduction to Machine Learning with Python]
> 
> 介绍:Python机器学习入门.
> 
>  *  [《Neural Enquirer: Learning to Query Tables with Natural Language》][Neural Enquirer_ Learning to Query Tables with Natural Language]
> 
> 介绍:Neural Enquirer 第二版.
> 
>  *  [《Deep Learning - Taking machine learning to the next level》][Deep Learning - Taking machine learning to the next level]
> 
> 介绍:\[Google\]基于TensorFlow的深度学习/机器学习课程.
> 
>  *  [《100 “must read” R-bloggers’ posts for 2015》][100 _must read_ R-bloggers_ posts for 2015]
> 
> 介绍:R-bloggers网站2015"必读"的100篇文章,R语言学习的福音.
> 
>  *  [《Machine Learning: a Probabilistic Perspective》][Machine Learning_ a Probabilistic Perspective]
> 
> 介绍:推荐书籍:<机器学习：概率视角>,样章[Undirected graphical models Markov random fields][].
> 
>  *  [《Deep learning Book》][Deep Learning_An MIT Press Book]
> 
> 介绍:这是一本在线的深度学习书籍,合著者有Ian Goodfellow, Yoshua Bengio 和 Aaron Courville.如果你是一位新入门的学员可以先看这本书籍[Yoshua Bengio: How can one get started with machine learning?][Yoshua Bengio_ How can one get started with machine learning]
> 
>  *  [《UFLDL Recommended Readings》][UFLDL Recommended Readings]
> 
> 介绍:UFLDL推荐的深度学习阅读列表.
> 
>  *  [《CSE 705: Deep Learning (Spring 2015)》][CSE 705_ Deep Learning _Spring 2015]
> 
> 介绍:纽约州立大学布法罗分校2015年春季机器学习课程主页.
> 
>  *  [《Theano is a Deep learning Python library 》][Theano is a Deep learning Python library]
> 
> 介绍: Theano是主流的深度学习Python库之一，亦支持GPU,入门比较难.推荐[Theano tutorial][],[Document][]
> 
>  *  [《Statistical Language Models Based On Neural Networks》][Statistical Language Models Based On Neural Networks]
> 
> 介绍:博士论文:神经网络统计语言模型.
> 
>  *  [《文本数据的机器学习自动分类方法(上)》][Link 37]
> 
> 介绍:[文本数据的机器学习自动分类方法(下)][Link 38].
> 
>  *  [《Pixel Recurrent Neural Networks》][Pixel Recurrent Neural Networks]
> 
> 介绍:用RNN预测像素，可以把被遮挡的图片补充完整.
> 
>  *  [《Computational Network Toolkit (CNTK)》][Computational Network Toolkit _CNTK]
> 
> 介绍:微软研究院把其深度学习工具包CNTK,想进一步了解和学习CNTK的同学可以看前几天公布的《CNTK白皮书》[An Introduction to Computational Networks and the Computational Network Toolkit][].
> 
>  *  [《Kalman and Bayesian Filters in Python》][Kalman and Bayesian Filters in Python]
> 
> 介绍: 卡尔曼滤波器教材，用尽量少的数学和推导，传授直觉和经验，全部Python示例，内容覆盖卡尔曼滤波器、扩展卡尔曼滤波，无迹卡尔曼滤波等，包括练习和参考答案
> 
>  *  [《Statistical inference for data science》][Statistical inference for data science]
> 
> 介绍:在线免费书:面向数据科学的统计推断，R示例代码，很不错[GitHub][].
> 
>  *  [《Learning Deep Architectures for AI》][Learning Deep Architectures for AI]
> 
> 介绍:这本书是由Yoshua Bengio撰写的教程,其内容包含了学习人工智能所使用的深度学习架构的学习资源,书中的项目`已停止更新`[DeepLearnToolbox][DeepLearn Toolbox].
> 
>  *  [《Machine Learning Tutorials》][Machine Learning Tutorials]
> 
> 介绍:这是一份机器学习和深度学习教程，文章和资源的清单。这张清单根据各个主题进行撰写，包括了许多与深度学习有关的类别、计算机视觉、加强学习以及各种架构.
> 
>  *  [《Data science ipython notebooks》][Continually updated Data Science Python Notebooks]
> 
> 介绍:这是由Donne Martin策划收集的IPython笔记本。话题涵盖大数据、Hadoop、scikit-learn和科学Python堆栈以及很多其他方面的内容。至于深度学习，像是TensorFlow、Theano和Caffe之类的框架也均被涵盖其中，当然还有相关的特定构架和概念等.
> 
>  *  [《Open Source Deep Learning Server》][Open Source Deep Learning Server]
> 
> 介绍:开源的深度学习服务,DeepDetect是C++实现的基于外部机器学习/深度学习库（目前是Caffe）的API。给出了图片训练（ILSVRC）和文本训练（基于字的情感分析，NIPS15）的样例，以及根据图片标签索引到ElasticSearch中[github][github 2].
> 
>  *  [《Data Mining, Analytics, Big Data, and Data Science》][Data Mining_ Analytics_ Big Data_ and Data Science]
> 
> 介绍:这是国外的一个科技频道,涵盖了数据挖掘,分析以及数据科学类的文章.偶尔还有机器学习精选.
> 
>  *  [《Data Mining And Statistics: What's The Connection?》][Data Mining And Statistics_ What_s The Connection]
> 
> 介绍:经典论文:数据挖掘与统计学.
> 
>  *  [《(NIPS’2015 Tutorial)Yoshua Bengio深度学习》][NIPS_2015 Tutorial_Yoshua Bengio]
> 
> 介绍:NIPS’2015 Tutorial by Yoshua Bengio.
> 
>  *  [《(NENO:Python based Deep Learning Framework》][Python based Deep Learning Framework by Nervana]
> 
> 介绍:Nervana Systems的开源深度学习框架neon发布.
> 
>  *  [《(Matt Might:Reading for graduate students》][Matt Might_Reading for graduate students]
> 
> 介绍:犹他州大学Matt Might教授推荐的研究生阅读清单.
> 
>  *  [《Awesome Public Datasets》][Awesome Public Datasets]
> 
> 介绍:开放数据集.
> 
>  *  [《Introduction to Probability - The Science of Uncertainty》][Introduction to Probability - The Science of Uncertainty]
> 
> 介绍:(edX)不确定性的科学——概率论导论(MITx).
> 
>  *  [《R software and tools for everyday use》][R software and tools for everyday use]
> 
> 介绍:R语言开发常用软件/工具推荐.
> 
>  *  [《Implementing Dynamic memory networks》][Implementing Dynamic memory networks]
> 
> 介绍:动态记忆网络实现.
> 
>  *  [《Deeplearning4j 中文主页》][Deeplearning4j 1]
> 
> 介绍:英文[主页][DeepLearning4J]
> 
>  *  [《Big Data Analysis Learning Resources: 50 Courses, Blogs, Tutorials, And More For Mastering Big Data Analytics》][Big Data Analysis Learning Resources_ 50 Courses_ Blogs_ Tutorials_ And More For Mastering Big Data Analytics]
> 
> 介绍:50个大数据分析最佳学习资源(课程、博客、教程等)
> 
>  *  [《A Full Hardware Guide to Deep Learning》][A Full Hardware Guide to Deep Learning]
> 
> 介绍:深度学习的全面硬件指南，从GPU到RAM、CPU、SSD、PCIe,[译文][Link 39]
> 
>  *  [《Deep Residual Networks》][Deep Residual Networks]
> 
> 介绍:kaiming开源作品
> 
>  *  [《The Definitive Guide to Natural Language Processing》][The Definitive Guide to Natural Language Processing]
> 
> 介绍:自然语言处理(NLP)权威指南
> 
>  *  [《Evaluating language identification performance》][Evaluating language identification performance]
> 
> 介绍:如何在社会媒体上做语言检测？没有数据怎么办？推特官方公布了一个十分难得的数据集：12万标注过的Tweets，有70种语言
> 
>  *  [《ICLR 2016 Accepted Papers》][ICLR 2016 Accepted Papers]
> 
> 介绍:深度学习和机器学习重要会议ICLR 2016录取文章
> 
>  *  [《Machine Learning: An In-Depth, Non-Technical Guide - Part 1》][Machine Learning_ An In-Depth_ Non-Technical Guide - Part 1]
> 
> 介绍:机器学习——深度非技术指南
> 
>  *  [《Data Storytelling 101: Helpful Tools for Gathering Ideas, Designing Content & More》][Data Storytelling 101_ Helpful Tools for Gathering Ideas_ Designing Content _ More]
> 
> 介绍:数据叙事入门指南——创意生成/数据采集/内容设计相关资源推荐
> 
>  *  [《WikiTableQuestions: a Complex Real-World Question Understanding Dataset》][WikiTableQuestions_ a Complex Real-World Question Understanding Dataset]
> 
> 介绍:WikiTableQuestions——复杂真实问答数据集
> 
>  *  [《Big Data: 35 Brilliant And Free Data Sources For 2016》][Big Data_ 35 Brilliant And Free Data Sources For 2016]
> 
> 介绍:(2016版)35个超棒的免费大数据源
> 
>  *  [《SPARKNET: training deep networks in spark》][SPARKNET_ training deep networks in spark]
> 
> 介绍:Ion Stoica和 Michael I. Jordan两位大家首次联手发文，CAFFE和SPARK完美结合，分布式深度学习混搭模式！[github][github 3]
> 
>  *  [《DeepLearning.University – An Annotated Deep Learning Bibliography | Memkite》][DeepLearning.University _ An Annotated Deep Learning Bibliography _ Memkite]
> 
> 介绍:深度学习(分类)文献集
> 
>  *  [《Learning Deep Learning》][Learning Deep Learning]
> 
> 介绍:深度学习阅读列表
> 
>  *  [《Awesome42 The easiest way to find R packages》][Awesome42 The easiest way to find R packages]
> 
> 介绍:探索R包的好网站Awesome 42
> 
>  *  [《MLbase:Distributed Machine Learning Made Easy》][MLbase_Distributed Machine Learning Made Easy]
> 
> 介绍:MLbase是[Prof. Dr. Tim Kraska][]的一个研究项目，MLbase是一个分布式机器学习管理系统
> 
>  *  [《Deep Learning At Scale and At Ease》][Deep Learning At Scale and At Ease]
> 
> 介绍:分布式深度学习平台[SINGA][]介绍
> 
>  *  [《Learn All About Apache Spark (100x Faster than Hadoop MapReduce)》][Learn All About Apache Spark _100x Faster than Hadoop MapReduce]
> 
> 介绍:Spark视频集锦
> 
>  *  [《R For Deep Learning (I): Build Fully Connected Neural Network From Scratch》][R For Deep Learning _I_ Build Fully Connected Neural Network From Scratch]
> 
> 介绍:R语言深度学习第一节:从零开始
> 
>  *  [《A Visual Introduction to Machine Learning》][A Visual Introduction to Machine Learning]
> 
> 介绍:图解机器学习
> 
>  *  [《Citation Network Dataset》][Citation Network Dataset]
> 
> 介绍:AMiner论文引用数据集(v7:2,244,021 papers and 4,354,534 citation relationships)
> 
>  *  [《Best Free Machine Learning Ebooks》][Best Free Machine Learning Ebooks]
> 
> 介绍:10本最佳机器学习免费书
> 
>  *  [《International Conference on Computer Vision (ICCV) 2015, Santiago》][International Conference on Computer Vision _ICCV_ 2015_ Santiago]
> 
> 介绍:ICCV15视频集
> 
>  *  [《CaffeOnSpark Open Sourced for Distributed Deep Learning on Big Data Clusters》][CaffeOnSpark Open Sourced for Distributed Deep Learning on Big Data Clusters]
> 
> 介绍::(Yahoo)基于Hadoop/Spark的分布式Caffe实现CaffeOnSpark
> 
>  *  [《A Short Introduction to Learning to Rank》][A Short Introduction to Learning to Rank]
> 
> 介绍:Learning to Rank简介
> 
>  *  [《Global Deep learning researcher》][Global Deep learning researcher]
> 
> 介绍:全球深度学习专家列表,涵盖研究者主页
> 
>  *  [《Top Spark Ecosystem Projects》][Top Spark Ecosystem Projects]
> 
> 介绍:[Spark生态顶级项目汇总][Spark]
> 
>  *  [《Proceedings of the 21st International Conference on Intelligent User Interfaces》][Proceedings of the 21st International Conference on Intelligent User Interfaces]
> 
> 介绍:[ACM IUI'16][ACM IUI_16]论文集[Conference Navigator - Proceedings][]
> 
>  *  [《Machine Learning: An In-Depth, Non-Technical Guide - Part 1》][Machine Learning_ An In-Depth_ Non-Technical Guide - Part 1]
> 
> 介绍:深入机器学习,[2][],[3][],[4][]
> 
>  *  [《Oxford Deep Learning》][Oxford Deep Learning]
> 
> 介绍:[Nando de Freitas][]在 Oxford 开设的深度学习课程,[课程youtube地址][youtube],Google DeepMind的研究科学家,此外[首页:computervisiontalks][computervisiontalks]的内容也很丰富,如果你是做机器视觉方面的研究,推荐也看看其他内容.肯定收获也不小.还有,这位[youtube主页][youtube 1]顶过的视频也很有份量
> 
>  *  [《Neural Networks for Machine Learning》][Neural Networks for Machine Learning]
> 
> 介绍:Geoffrey Hinton在Coursera开设的MOOC
> 
>  *  [《Deep Learning News》][Deep Learning News 1]
> 
> 介绍:深度学习领域的Hacker news.紧跟深度学习的新闻、研究进展和相关的创业项目。从事机器学习,深度学习领域的朋友建议每天看一看
> 
>  *  [《Maxout Networks》][Maxout Networks]
> 
> 介绍:Maxout网络剖析
> 
>  *  [《Advances in Neural Information Processing Systems》][Advances in Neural Information Processing Systems]
> 
> 介绍:NIPS领域的会议paper集锦
> 
>  *  [《Machine learning applications in genetics and genomics》][Machine learning applications in genetics and genomics]
> 
> 介绍:机器学习在生物工程领域的应用,如果你从事生物工程领域,可以先阅读一篇文章[详细介绍][Link 40]
> 
>  *  [《Deep Learning in Bioinformatics》][Deep Learning in Bioinformatics]
> 
> 介绍:深度学习在生物信息学领域的应用
> 
>  *  [《A Few Useful Things to Know about Machine Learning》][A Few Useful Things to Know about Machine Learning]
> 
> 介绍:一些关于机器学习需要知道知识,对于刚刚入门机器学习的同学应该读一读
> 
>  *  [《Cambridge Machine Learning Group》][Cambridge Machine Learning Group]
> 
> 介绍:剑桥大学机器学习用户组主页,网罗了剑桥大学一些机器学习领域专家与新闻
> 
>  *  [《Randy Olson's data analysis and machine learning projects》][Randy Olson_s data analysis and machine learning projects]
> 
> 介绍:[Randy Olson's][Randy Olson_s]的一些数据分析与机器学习项目库,是学习实践的好材料
> 
>  *  [《GoLearn:Golang machine learning library》][GoLearn_Golang machine learning library]
> 
> 介绍:Golang机器学习库,简单,易扩展
> 
>  *  [《Swift Ai》][Swift Ai]
> 
> 介绍:用Swift开发苹果应用的倒是很多，而用来做机器学习的就比较少了.Swift Ai在这方面做了很多聚集.可以看看
> 
>  *  [《Please explain Support Vector Machines (SVM) like I am a 5 year old》][Please explain Support Vector Machines _SVM_ like I am a 5 year old]
> 
> 介绍:如何向一位5岁的小朋友解释支持向量机(SVM)
> 
>  *  [《reddit Machine learning》][reddit Machine learning]
> 
> 介绍: reddit的机器学习栏目
> 
>  *  [《ComputerVision resource》][ComputerVision resource]
> 
> 介绍: 计算机视觉领域的一些牛人博客，超有实力的研究机构等的网站链接.做计算机视觉方向的朋友建议多关注里面的资源
> 
>  *  [《Multimedia Laboratory Homepage》][Multimedia Laboratory Homepage]
> 
> 介绍:香港中文大学深度学习研究主页,此外研究小组对[2013年deep learning 的最新进展和相关论文][2013_deep learning]做了整理,其中useful links的内容很受益
> 
>  *  [《Search Engines that Learn from Their Users》][Search Engines that Learn from Their Users]
> 
> 介绍: 这是一篇关于搜索引擎的博士论文,对现在普遍使用的搜索引擎google，bing等做了分析.对于做搜索类产品的很有技术参考价值
> 
>  *  [《Deep Learning Books》][Deep Learning Books]
> 
> 介绍: 深度学习书籍推荐(毕竟这类书比较少).
> 
>  *  [《Deep Learning Books》][Deep Learning Books]
> 
> 介绍: 深度学习书籍推荐(毕竟这类书比较少).
> 
>  *  [《Towards Bayesian Deep Learning: A Survey》][Towards Bayesian Deep Learning_ A Survey]
> 
> 介绍: 贝叶斯定理在深度学习方面的研究论文.
> 
>  *  [《Revisiting Distributed Synchronous SGD》][Revisiting Distributed Synchronous SGD]
> 
> 介绍: 来自谷歌大脑的重温分布式梯度下降.同时推荐[大规模分布式深度网络][Link 41]
> 
>  *  [《Research Issues in Social Computing》][Research Issues in Social Computing]
> 
> 介绍: 社交计算研究相关问题综述.
> 
>  *  [《What are some important areas of research in social computing right now?》][What are some important areas of research in social computing right now]
> 
> 介绍: 社交计算应用领域概览,里面有些经典论文推荐
> 
>  *  [《Collaborative Filtering Recommender Systems》][Collaborative Filtering Recommender Systems]
> 
> 介绍: 协同过滤在推荐系统应用.
> 
>  *  [《Content-Boosted Collaborative Filtering for Improved Recommendations》][Content-Boosted Collaborative Filtering for Improved Recommendations]
> 
> 介绍: 协同过滤在内容推荐的研究.
> 
>  *  [《Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion》][Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion]
> 
> 介绍: 协同过滤经典论文.
> 
>  *  [《Item-based Collaborative Filtering Recommendation Algorithms》][Item-based Collaborative Filtering Recommendation Algorithms]
> 
> 介绍: 协同过滤算法.
> 
>  *  [《Amazon.com Recommendations Item-to-Item Collaborative Filtering》][Amazon.com Recommendations Item-to-Item Collaborative Filtering]
> 
> 介绍: 亚马逊对于协同过滤算法应用.
> 
>  *  [《Collaborative Filtering for Implicit Feedback Datasets》][Collaborative Filtering for Implicit Feedback Datasets]
> 
> 介绍: 协同过滤的隐式反馈数据集处理.
> 
>  *  [《Tutorials, papers and code for computer graphics, fractals and demoscene》][Tutorials_ papers and code for computer graphics_ fractals and demoscene]
> 
> 介绍: 计算机图形，几何等论文，教程，代码.做计算机图形的推荐收藏.
> 
>  *  [《ELEN 6886 Sparse Representation and High-Dimensional Geometry》][ELEN 6886 Sparse Representation and High-Dimensional Geometry]
> 
> 介绍: 推荐哥伦比亚大学课程，稀疏表示和高维几何.12年由Elsevier、13年至今由PAMI(仍由Elsevier赞助)设立的青年研究者奖(Young Researcher Award)授予完成博士学位后七年内取得杰出贡献的；由CV社区提名，在CVPR会议上宣布。2015年得主是哥大助理教授John Wright，09年[《健壮人脸识别的稀疏表示法》][Link 42]引用已超5K.
> 
>  *  [《Software engineer how to learning Machine learning》][Software engineer how to learning Machine learning]
> 
> 介绍: CMU机器学习系著名教授Alex Smola在Quora对于《程序员如何学习Machine Learning》的建议：Alex推荐了不少关于线性代数、优化、系统、和统计领域的经典教材和资料.
> 
>  *  [《Book review: Fundamentals of Deep Learning》][Book review_ Fundamentals of Deep Learning]
> 
> 介绍: 书籍推荐,深度学习基础.[源码][Link 43]
> 
>  *  [《Learning from Big Code》][Learning from Big Code]
> 
> 介绍: 软件工程领域现在也对机器学习和自然语言处理很感兴趣，有人推出了“大代码”的概念，分享了不少代码集合，并且觉得ML可以用在预测代码Bug，预测软件行为，自动写新代码等任务上。大代码数据集下载
> 
>  *  [《Object Detection》][Object Detection]
> 
> 介绍: 深度学习进行目标识别的资源列表：包括RNN、MultiBox、SPP-Net、DeepID-Net、Fast R-CNN、DeepBox、MR-CNN、Faster R-CNN、YOLO、DenseBox、SSD、Inside-Outside Net、G-CNN
> 
>  *  [《Deep Learning: Course by Yann LeCun at Collège de France 2016(Slides in English)》][Deep Learning_ Course by Yann LeCun at Coll_ge de France 2016_Slides in English]
> 
> 介绍: Yann LeCun 2016深度学习课程的幻灯片（Deep Learning Course by Yann LeCun at Collège de France 2016）[百度云][Link 44]密码: cwsm [原地址][Link 45]
> 
>  *  [《Stanford HCI Group》][Stanford HCI Group]
> 
> 介绍: 斯坦福人机交互组五篇CHI16文章。1.众包激励机制的行为经济学研究：批量结算比单任务的完成率高。2.在众包专家和新手间建立联系：微实习。3.词嵌入结合众包验证的词汇主题分类（如猫、狗属于宠物）。4.词嵌入结合目标识别的活动预测。5.鼓励出错以加快众包速度。
> 
>  *  [《Learn Data Science》][Learn Data Science]
> 
> 介绍: 自学数据科学
> 
>  *  [《CS224D Lecture 7 - Introduction to TensorFlow》][CS224D Lecture 7 - Introduction to TensorFlow]
> 
> 介绍: 本课是[CS224D][]一节介绍TensorFlow课程，[ppt][ppt 1],[DeepDreaming with TensorFlow][]
> 
>  *  [《Leaf - Machine Learning for Hackers》][Leaf - Machine Learning for Hackers]
> 
> 介绍: Leaf是一款机器学习的开源框架，专为黑客打造，而非为科学家而作。它用Rust开发，传统的机器学习，现今的深度学习通吃。[Leaf][]
> 
>  *  [《MXnet:Flexible and Efficient library for deep learning》][MXnet_Flexible and Efficient library for deep learning]
> 
> 介绍: [GTC 2016][]视频，MXnet的手把手深度学习tutorial,相关参考资料[MXNet Tutorial for NVidia GTC 2016.][]
> 
>  *  [《OpenAI Gym: Toolkit for developing, comparing reinforcement learning algorithms》][OpenAI Gym_ Toolkit for developing_ comparing reinforcement learning algorithms]
> 
> 介绍: OpenAI Gym：开发、比较强化学习算法工具箱
> 
>  *  [《conference-iclr-2016 Papers and Code》][conference-iclr-2016 Papers and Code]
> 
> 介绍: 机器学习会议ICLR 2016 论文的代码集合
> 
>  *  [《probabilistic graphical models principles and techniques》][probabilistic graphical models principles and techniques]
> 
> 介绍: 此书是斯坦福大学概率图模型大牛Daphne Koller所写，主要涉及的是贝叶斯网络和马尔科夫逻辑网络的learning和inference问题，同时又对PGM有深刻的理论解释，是学习概率图模型必看的书籍。难度中上，适合有一些ML基础的研究生.[备份地址][Link 46]
> 
>  *  [《Information Theory, Inference, and Learning Algorithms》][Information Theory_ Inference_ and Learning Algorithms]
> 
> 介绍: 此书是剑桥大学著名信息论专家David MacKay所写，出发角度与很多机器学习的书籍都不一样，inference和MCMC那章写的最好，难度中。适合研究生和本科生。
> 
>  *  [《Convex Optimization – Boyd and Vandenberghe》][Convex Optimization _ Boyd and Vandenberghe]
> 
> 介绍: 非常好的Convex Optimization教材，覆盖了各种constrained和unconstrained optimization方法，介绍了convex优化的基本概念和理论。难度中，适合对优化和机器学习有一定基础的人群
> 
>  *  [《Machine - Learning - Tom Mitchell》][Machine - Learning - Tom Mitchell]
> 
> 介绍: 本书是CMU机器学习系主任Mitchell早年写的机器学习教科书，年代有点久远.难度不高。适合初学者，本科生，研究生
> 
>  *  [《Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond》][Learning with Kernels Support Vector Machines_ Regularization_ Optimization_ and Beyond]
> 
> 介绍: 本书设计学习Kernel和SVM的各种理论基础，需要较强的数学功底，适合对kernel和SVM感兴趣的同学选读[Learning with Kernels PPT][]，参考[Learning with Kernels][]
> 
>  *  [《The Elements of Statistical Learning: Data Mining, Inference, and Prediction》][The Elements of Statistical Learning_ Data Mining_ Inference_ and Prediction]
> 
> 介绍: 斯坦福统计系三位大神的统计学习教科书，偏统计和学习理论，需要对线性代数、统计和概率论有一定基础、难度高、适合研究生
> 
>  *  [《Data Mining: Practical Machine Learning Tools and Techniques》][Data Mining_ Practical Machine Learning Tools and Techniques]
> 
> 介绍: 本书是著名机器学习工具Weka作者撰写的应用机器学习指导书、非常实用、难度低、适合文科和各种应用科学做参考
> 
>  *  [《Foundations of Statistical Natural Language Processing》][Foundations of Statistical Natural Language Processing]
> 
> 介绍: 本书也是一本比较受欢迎的NLP教科书，难度一般，主要覆盖统计NLP方法，是斯坦福的另一位大牛Chirs manning所写
> 
>  *  [《Speech and Language Processing》][Speech and Language Processing]
> 
> 介绍: 在北美NLP最常用的教材，斯坦福Jurafsky所写的自然语言处理入门教程，覆盖面较为全面，难度中低。适合本科生和研究生
> 
>  *  [《Natural Language Processing with Python - NLTK》][Natural Language Processing with Python - NLTK]
> 
> 介绍: 实战型教程，著名工具NLTK作者的著作，适合本科生和入门者边动手边学

参考文献：

1.深度学习阅读清单：http://suanfazu.com/t/topic/245

2.深度学习如何入门：https://www.zhihu.com/question/26006703/answer/63572833

转自[https://www.cnblogs.com/charlotte77/p/5485438.html][https_www.cnblogs.com_charlotte77_p_5485438.html]

[Deep Learning66]: http://www.iro.umontreal.ca/~bengioy/dlbook/
[Neural Networks and Deep Learning42]: http://neuralnetworksanddeeplearning.com/
[Deep Learning27]: http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf
[Deep Learning Tutorial23]: http://deeplearning.net/tutorial/deeplearning.pdf
[Deep Learning_An MIT Press Book]: http://www.deeplearningbook.org/
[Machine Learning10]: https://class.coursera.org/ml-005
[Neural Networks for Machine Learning12]: https://class.coursera.org/neuralnets-2012-001
[Neural networks class2]: https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
[Deep Learning Course14]: http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start
[How To Create A Mind3]: https://www.youtube.com/watch?v=RIkxVci-R4k
[Deep Learning_ Self-Taught Learning and Unsupervised Feature Learning2]: https://www.youtube.com/watch?v=n1ViNeWhC24
[Recent Developments in Deep Learning2]: https://www.youtube.com/watch?v=vShMxxqtDDs&amp;index=3&amp;list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT
[The Unreasonable Effectiveness of Deep Learning]: https://www.youtube.com/watch?v=sc-KbuZqGkI
[Deep Learning of Representations]: https://www.youtube.com/watch?v=4xsVFLnHC_0
[Principles of Hierarchical Temporal Memory]: https://www.youtube.com/watch?v=6ufPpZDmPKA
[Machine Learning Discussion Group - Deep Learning w_ Stanford AI Lab1]: https://www.youtube.com/watch?v=2QJi0ArLq7s&amp;list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT
[Making Sense of the World with Deep Learning1]: http://vimeo.com/80821560
[Demystifying Unsupervised Feature Learning]: https://www.youtube.com/watch?v=wZfVBwOO0-k
[Visual Perception with Deep Learning3]: https://www.youtube.com/watch?v=3boKlkPBckA
[Oxford Deep Learning_-]: http://link.zhihu.com/?target=https%3A//class.coursera.org/neuralnets-2012-001/
[ImageNet Classification with Deep Convolutional Neural Networks5]: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
[Using Very Deep Autoencoders for Content Based Image Retrieval2]: http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf
[Learning Deep Architectures for AI2]: http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf
[CMU_s list of papers7]: http://deeplearning.cs.cmu.edu/
[The Learning Machines]: https://link.zhihu.com/?target=http%3A//www.nature.com/news/computer-science-the-learning-machines-1.14481
[Deep Learning]: http://link.zhihu.com/?target=http%3A//www.nature.com/articles/nature14539.epdf%3Freferrer_access_token%3DK4awZz78b5Yn2_AoPV_4Y9RgN0jAjWel9jnR3ZoTv0PU8PImtLRceRBJ32CtadUBVOwHuxbf2QgphMCsA6eTOw64kccq9ihWSKdxZpGPn2fn3B_8bxaYh0svGFqgRLgaiyW6CBFAb3Fpm6GbL8a_TtQQDWKuhD1XKh_wxLReRpGbR_NdccoaiKP5xvzbV-x7b_7Y64ZSpqG6kmfwS6Q1rw%253D%253D%26tracking_referrer%3Dwww.nature.com
[Growing Pains in Deep Learning]: http://link.zhihu.com/?target=http%3A//cacm.acm.org/news/188737-growing-pains-for-deep-learning/fulltext
[Deep Learning in Neural Networks]: http://link.zhihu.com/?target=http%3A//arxiv.org/pdf/1404.7828.pdf
[UFLDL Tutorial 120]: http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial
[Deep Learning Tutorial from Stanford]: http://ufldl.stanford.edu/tutorial/
[Deep Learning for NLP _without Magic_8]: http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
[A Deep Learning Tutorial_ From Perceptrons to Deep Networks5]: http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks
[deeplearning.net7]: http://deeplearning.net/
[deeplearning.stanford.edu6]: http://deeplearning.stanford.edu/
[Forum2]: http://suanfazu.com/
[MNIST1]: http://yann.lecun.com/exdb/mnist/
[Google House Numbers]: http://ufldl.stanford.edu/housenumbers/
[CIFAR-10 and CIFAR-1003]: http://www.cs.toronto.edu/~kriz/cifar.html
[IMAGENET1]: http://www.image-net.org/
[Tiny Images1]: http://groups.csail.mit.edu/vision/TinyImages/
[Flickr Data]: http://yahoolabs.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images
[Berkeley Segmentation Dataset 500]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
[Caffe9]: http://caffe.berkeleyvision.org/
[Torch73]: http://torch.ch/
[Theano3]: http://deeplearning.net/software/theano/
[cuda-convnet2]: https://code.google.com/p/cuda-convnet2/
[Ccv1]: http://libccv.org/doc/doc-convnet/
[NuPIC3]: http://numenta.org/nupic.html
[DeepLearning4J]: http://deeplearning4j.org/
[Google Plus - Deep Learning Community]: https://plus.google.com/communities/112866381580457264725
[Caffe Webinar4]: http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php?searchByKeyword=shelhamer&amp;searchItems=&amp;sessionTopic=&amp;sessionEvent=4&amp;sessionYear=2014&amp;sessionFormat=&amp;submit=&amp;select=+
[100 Best Github Resources in Github for DL5]: http://meta-guide.com/software-meta-guide/100-best-github-deep-learning/
[Word2Vec3]: https://code.google.com/p/word2vec/
[Caffe DockerFile2]: https://registry.hub.docker.com/u/tleyden5iwx/caffe/
[TorontoDeepLEarning convnet]: https://github.com/TorontoDeepLearning/convnet
[Vision data sets1]: http://www.cs.cmu.edu/~cil/v-images.html
[Fantastic Torch Tutorial4]: http://code.cogbits.com/wiki/doku.php
[gfx.js1]: https://github.com/clementfarabet/gfx.js
[DeepLearn Toolbox]: https://github.com/rasmusbergpalm/DeepLearnToolbox
[GitHub - Eniac-Xie_PyConvNet_ Convolutional Neural Network for python users]: https://link.zhihu.com/?target=https://github.com/Eniac-Xie/PyConvNet
[Video Recognition]: http://link.zhihu.com/?target=http%3A//www.clarifai.com/
[Self-Driving Cars]: http://link.zhihu.com/?target=http%3A//blogs.nvidia.com/blog/2015/02/24/deep-learning-drive/
[Natural Language Processing]: http://link.zhihu.com/?target=http%3A//devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/
[And others]: http://link.zhihu.com/?target=http%3A//research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf
[H2O]: http://link.zhihu.com/?target=http%3A//0xdata.com/product/deep-learning/
[Deeplearning4j]: http://link.zhihu.com/?target=http%3A//deeplearning4j.org/
[Caffe]: http://link.zhihu.com/?target=http%3A//caffe.berkeleyvision.org/
[Theano]: http://link.zhihu.com/?target=http%3A//deeplearning.net/software/theano/
[Deep Learning News]: http://link.zhihu.com/?target=http%3A//news.startup.ml/
[Machine Learning_Deep Learning_Chapter 2]: https://github.com/ty4z2008/Qix/blob/master/dl2.md
[Image Scaling using Deep Convolutional Neural Networks]: http://engineering.flipboard.com/2015/05/scaling-convnets/
[Proceedings of The 32nd International Conference on Machine Learning]: http://jmlr.org/proceedings/papers/v37/
[ICML2015 Sessions]: http://icml.cc/2015/?page_id=825
[Microsoft researchers accelerate computer vision accuracy and improve 3D scanning models]: http://blogs.technet.com/b/inside_microsoft_research/archive/2015/06/08/microsoft-researchers-accelerate-computer-vision-accuracy-and-improve-3d-scanning-models.aspx
[Machine Learning for Humans]: https://github.com/marcotcr/mlforhumans
[A Plethora of Tools for Machine Learning]: http://knowm.org/machine-learning-tools-an-overview/
[The art of visualizing visualizations_ a best practice guide]: http://sapblog.be/en/the-art-of-visualizing-visualizations-a-best-practice-guide/
[MIT Machine Learning for Big Data and Text Processing Class Notes - Day 1]: http://blog.adnanmasood.com/2015/06/08/mit-machine-learning-for-big-data-and-text-processing-class-notes-day-1/
[Day 2]: http://blog.adnanmasood.com/2015/06/09/mit-machine-learning-for-big-data-and-text-processing-class-notes-day-2/
[Day 3]: http://blog.adnanmasood.com/2015/06/11/mit-machine-learning-for-big-data-and-text-processing-class-notes-day-3/
[Day 4]: http://blog.adnanmasood.com/2015/06/12/mit-machine-learning-for-big-data-and-text-processing-class-notes-day-4/
[Day 5]: http://blog.adnanmasood.com/2015/06/12/mit-machine-learning-for-big-data-and-text-processing-class-notes-day-5/
[Getting _deep_ about _deep learning]: http://whatsnext.nuance.com/in-the-labs/what-is-deep-machine-learning/
[Mixture Density Networks]: http://blog.otoro.net/2015/06/14/mixture-density-networks/
[Interview Questions for Data Scientist Positions]: https://medium.com/@D33B/interview-questions-for-data-scientist-positions-5ad3c5d5b8bd
[Accurately Measuring Model Prediction Error]: http://scott.fortmann-roe.com/docs/MeasuringError.html
[Continually updated Data Science Python Notebooks]: https://github.com/donnemartin/data-science-ipython-notebooks
[How to share data with a statistician]: https://github.com/jtleek/datasharing
[The Eyescream Project NeuralNets dreaming natural images]: http://soumith.ch/eyescream/
[A Neural Conversational Model]: http://arxiv.org/abs/1506.05869
[The 50 Best Masters in Data Science]: http://www.datasciencecentral.com/profiles/blogs/the-50-best-masters-in-data-science
[NLP]: http://forum.memect.com/thread/nlp%E5%B8%B8%E7%94%A8%E4%BF%A1%E6%81%AF%E8%B5%84%E6%BA%90/
[Conditional Random Fields as Recurrent Neural Networks]: http://www.robots.ox.ac.uk/~szheng/papers/CRFasRNN.pdf
[Link 1]: http://www.robots.ox.ac.uk/~szheng/crfasrnndemo
[Fully Convolutional Networks for Semantic Segmentation]: http://www.cs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf
[Link 2]: https://github.com/BVLC/caffe/wiki/Model-Zoo#fcn
[Growing Pains for Deep Learning]: http://cacm.acm.org/news/188737-growing-pains-for-deep-learning/fulltext
[Clustering Text Data Streams _ A Tree based Approach with Ternary Function and Ternary Feature Vector]: http://www.sciencedirect.com/science/article/pii/S1877050914005274
[Foundations and Advances in Data Mining]: http://cs.ucla.edu/~wwc/course/cs245a/mining%20book.pdf
[The Deep Learning Revolution_ Rethinking Machine Learning Pipelines]: http://www.infoq.com/presentations/deep-learning
[The Definitive Guide to Do Data Science for Good]: http://blog.datalook.io/definitive-guide-data-science-good/
[Microsoft Academic Graph]: http://research.microsoft.com/en-us/projects/mag/
[Challenges and Opportunities Of Machine Learning In Production]: https://www.youtube.com/watch?v=UEwDwTkWwdc&hd=1
[Neural Nets for Newbies]: https://www.youtube.com/watch?v=Cu6A96TUy_o
[A Nearly-Linear Time Framework for Graph-Structured Sparsity]: http://jmlr.org/proceedings/papers/v37/hegde15.pdf
[Optimal and Adaptive Algorithms for Online Boosting]: http://jmlr.org/proceedings/papers/v37/beygelzimer15.pdf
[Top 20 Python Machine Learning Open Source Projects]: http://www.kdnuggets.com/2015/06/top-20-python-machine-learning-open-source-projects.html
[The Parallel C_ Statistical Library for Bayesian Inference_ QUESO]: http://arxiv.org/abs/1507.00398
[github code]: http://libqueso.com/
[Deep learning_Yann LeCun_ Yoshua Bengio_ Geoffrey Hinton _2015_]: http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html
[Critique of Paper by _Deep Learning Conspiracy_ _Nature 521 p 436]: http://people.idsia.ch/~juergen/deep-learning-conspiracy.html
[Palladium]: https://github.com/ottogroup/palladium
[Advances in Structured Prediction]: http://hunch.net/~l2s/merged.pdf
[100 open source Big Data architecture papers for data professionals]: https://www.linkedin.com/pulse/100-open-source-big-data-architecture-papers-anil-madan
[Link 3]: http://www.csdn.net/article/2015-07-07/2825148/1
[Social Media _ Text Analytics]: http://socialmedia-class.org/syllabus.html
[Machine Learning for Developers]: http://xyclade.github.io/MachineLearning/
[Hot news detection using Wikipedia]: http://hameddaily.blogspot.com/2015/06/hot-news-detection-using-wikipedia_29.html
[Harvard Intelligent Probabilistic Systems Group]: https://github.com/HIPS
[An Empirical Exploration of Recurrent Network Architectures]: http://jmlr.org/proceedings/papers/v37/jozefowicz15.html
[Efficient Training of LDA on a GPU by Mean-for-Mode Estimation]: http://jmlr.org/proceedings/papers/v37/tristan15.html
[From the Lab to the Factory_ Building a Production Machine Learning Infrastructure]: https://www.youtube.com/watch?v=v-91JycaKjc&hd=1
[6 Useful Databases to Dig for Data _and 100 more]: http://piktochart.com/6-useful-databases-to-dig-for-data/
[Deep Networks for Computer Vision at Google _ ILSVRC2014]: http://www.computervisiontalks.com/deep-networks-for-computer-vision-at-google/
[How to choose a machine learning API to build predictive apps]: https://medium.com/@louisdorard/developer-considerations-for-choosing-a-machine-learning-api-20e2de15eb3a
[Exploring the shapes of stories using Python and sentiment APIs]: https://indico.io/blog/plotlines/
[Movie selection using R]: http://melodywolk.com/2015/07/21/movie-selection-using-r/
[Link 4]: http://freakonometrics.hypotheses.org/20002
[A Tutorial on Graph-based Semi-Supervised Learning Algorithms for NLP]: http://graph-ssl.wdfiles.com/local--files/blog%3A_start/graph_ssl_acl12_tutorial_slides_final.pdf
[Arbitrariness of peer review_ A Bayesian analysis of the NIPS experiment]: http://arxiv.org/abs/1507.06411
[Basics of Computational Reinforcement Learning]: http://videolectures.net/rldm2015_littman_computational_reinforcement/
[Deep Reinforcement Learning]: http://videolectures.net/rldm2015_silver_reinforcement_learning/
[On Explainability of Deep Neural Networks]: http://blog.adnanmasood.com/2015/07/31/on-explainability-of-deep-neural-networks/
[The Essential Spark Cheat Sheet]: http://info.mapr.com/rs/mapr/images/rd204-010d-spark_0.pdf
[Machine Learning for Sports and Real Time Predictions]: http://www.thetalkingmachines.com/blog/2015/7/30/machine-learning-for-sports-and-real-time-predictions
[CS224W_ Social and Information Network Analysis Autumn 2014]: http://web.stanford.edu/class/cs224w/index.html
[Link 5]: http://web.stanford.edu/class/cs224w/handouts.html
[Link 6]: http://web.stanford.edu/class/cs224w/projects.html
[Link 7]: http://web.stanford.edu/class/cs224w/resources.html
[RL Course by David Silver]: https://www.youtube.com/playlist?list=PL5X3mDkKaJrL42i_jhE4N-p6E2Ol62Ofa
[slide]: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
[Faster deep learning with GPUs and Theano]: http://blog.dominodatalab.com/gpu-computing-and-deep-learning/
[Introduction to R Programming]: https://www.edx.org/course/introduction-r-programming-microsoft-dat204x
[Golang_Web Server For Performing Sentiment Analysis]: https://github.com/cdipaolo/sentiment-server
[A Beginner_s Guide to Restricted Boltzmann Machines]: http://deeplearning4j.org/restrictedboltzmannmachine.html
[KDD2015]: http://www.kdd.org/kdd2015/program.html
[Mining and Summarizing Customer Reviews]: http://www.cs.uic.edu/~liub/publications/kdd04-revSummary.pdf
[Mining High-Speed Data Streams]: http://homes.cs.washington.edu/~pedrod/papers/kdd00.pdf
[Optimizing Search Engines using Clickthrough Data]: http://www.cs.cornell.edu/people/tj/publications/joachims_02c.pdf
[Nvidia Deep Learning Courses]: http://www.hellophp.cn/archives/733
[Deep Learning Summer School 2015]: https://sites.google.com/site/deeplearningsummerschool/
[Link 8]: http://www.iro.umontreal.ca/~memisevr
[Link 9]: http://www.cvrobot.net/image-recognition-progression-based-on-deep-learning-by-baidu/
[Link 10]: http://www.ccf.org.cn/sites/ccf/xhdtnry.jsp?contentId=2857471255804
[Machine Learning Methods in Video Annotation]: http://rnd.azoft.com/machine-learning-methods-video-annotation/
[Training Recurrent Neural Networks]: http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
[Link 11]: http://www.csdn.net/article/2015-08-17/2825471
[Machine Learning Libraries in GoLang by Category]: http://www.fodop.com/ar-1002
[A Statistical View of Deep Learning]: http://blog.shakirm.com/wp-content/uploads/2015/07/SVDL.pdf
[Deep Learning For NLP - Tips And Techniques]: http://www.researchgate.net/publication/279853751_DEEP_LEARNING_FOR_NLP_-_TIPS_AND_TECHNIQUES
[CrowdFlower Competition Scripts_ Approaching NLP]: http://blog.kaggle.com/2015/08/18/crowdflower-scripts-approaching-nlp/
[CS224U_ Natural Language Understanding]: http://web.stanford.edu/class/cs224u/index.html
[Deep Learning and Shallow Learning]: http://freemind.pluskid.org/machine-learning/deep-learning-and-shallow-learning/
[A First Encounter with Machine Learning]: http://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf
[Max Welling]: http://www.ics.uci.edu/~welling/
[Click Models for Web Search]: http://clickmodels.weebly.com/uploads/5/2/2/5/52257029/mc2015-clickmodels.pdf
[Hinton CSC321_Deep Learning_Notes on CNN_Python_Theano_CUDA_OpenCV_...]: http://www.cnblogs.com/shouhuxianjian/p/4529235.html
[Which Algorithm Family Can Answer My Question]: http://blogs.technet.com/b/machinelearning/archive/2015/09/01/which-algorithm-family-can-answer-my-question.aspx
[Free Data Science Books]: http://www.learndatasci.stfi.re/free-books/
[Tutorial 4_ Deep Learning for Speech Generation and Synthesis]: http://www.superlectures.com/iscslp2014/tutorial-4-deep-learning-for-speech-generation-and-synthesis
[Link 12]: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42624.pdf
[Link 13]: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43893.pdf
[The Art of Data Science]: https://leanpub.com/artofdatascience
[Pattern Recognition and Machine Learning]: http://research.microsoft.com/en-us/um/people/cmbishop/prml/
[Link 14]: https://www.dropbox.com/s/sx95jq7n7zerrjl/PRML_Translation.pdf?dl=0
[Link 15]: http://pan.baidu.com/s/1hqheD5E
[an introduction to visualizing DATA]: http://piksels.com/wp-content/uploads/2009/01/visualizingdata.pdf
[That_s So Annoying_ A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using _petpeeve Tweets]: https://www.cs.cmu.edu/~yww/papers/emnlp2015petpeeves.pdf
[Link 16]: https://www.cs.cmu.edu/~yww/data/petpeeves.zip
[26 Things I Learned in the Deep Learning Summer School]: http://www.marekrei.com/blog/26-things-i-learned-in-the-deep-learning-summer-school/
[Data-Visualization Tools _ Books]: http://keshif.me/demo/VisTools
[Machine Learning and Probabilistic Graphical Models Course]: http://www.cedar.buffalo.edu/~srihari/CSE574/
[Understanding Machine Learning_ From Theory to Algorithms]: http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/index.html
[Machine Learning Checklist]: http://machinelearningmastery.com/machine-learning-checklist/
[NLP 1]: http://www.zhihu.com/question/32318281
[Link 17]: http://www.csdn.net/article/2015-09-08/2825647
[Gradient Boosted Regression Trees]: http://nbviewer.ipython.org/github/pprett/pydata-gbrt-tutorial/blob/master/gbrt-tutorial.ipynb
[slide 1]: http://orbi.ulg.ac.be/bitstream/2268/163521/1/slides.pdf
[Apache SINGA _ Distributed Deep Learning System]: http://www.comp.nus.edu.sg/~dbsystem/singa/
[E-commerce Recommendation with Personalized Promotion]: http://dl.acm.org/citation.cfm?id=2800178
[Scalable Machine Learning]: https://www.edx.org/course/scalable-machine-learning-uc-berkeleyx-cs190-1x
[Link 18]: http://www.52ml.net/star
[Automatic Summarization]: http://www.cis.upenn.edu/~nenkova/1500000015-Nenkova.pdf
[McKeown]: http://www.cis.upenn.edu/~nenkova/
[Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing]: http://www.emnlp2015.org/proceedings/EMNLP/index.html
[Recommender Systems _Machine Learning Summer School 2014 _ CMU]: http://www.slideshare.net/xamat/recommender-systems-machine-learning-summer-school-2014-cmu
[BigData Stream Mining]: http://www.ecmlpkdd2015.org/sites/default/files/ECMLPKDD2015Slides.pdf
[ECML PKDD 2015 Tutorial]: http://www.ecmlpkdd2015.org/program/tutorial-list
[Deep learning on Spark with Keras]: https://github.com/maxpumperla/elephas
[Keras]: https://github.com/fchollet/keras
[Prof. Surya Ganguli - The statistical physics of deep learning]: https://www.youtube.com/watch?v=7KCWcx-YIRI&hd=1
[...]: http://cmlakhan.github.io/courses/videos.html
[Introduction to Topic Modeling in Python]: http://chdoig.github.io/pytexas2015-topic-modeling/
[Large Scale Distributed Deep Learning on Hadoop Clusters]: http://yahoohadoop.tumblr.com/post/129872361846/large-scale-distributed-deep-learning-on-hadoop/
[Top Deep Learning Employers Based On LinkedIn Data]: http://www.vordot.com/deep-learning-employers-w-12020/
[Neural Net in C_ Tutorial]: https://vimeo.com/19569529
[Large-scale CelebFaces Attributes _CelebA_ Dataset]: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
[Unsupervised Feature Learning in Computer Vision]: https://www.cs.nyu.edu/web/Research/Theses/goroshin_ross.pdf
[Ross Goroshin_s webpage]: https://cs.nyu.edu/~goroshin/
[Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks]: http://arxiv.org/pdf/1506.03099v3.pdf
[Essential Machine Learning Algorithms in a nutshell]: https://manish.wordpress.com/2015/10/02/essential-machine-learning-algorithms-in-a-nutshell/
[A Huge List of Machine Learning And Statistics Repositories]: http://blog.josephmisiti.com/a-huge-list-of-machine-learning-repositories/
[Information Processing and Learning]: http://www.cs.cmu.edu/~aarti/Class/10704_Spring15/lecs.html
[Samy Bengio]: http://bengio.abracadoudou.com/
[Hadoop]: http://www.csdn.net/article/2015-10-01/2825840
[Learning both Weights and Connections for Efficient Neural Networks]: http://arxiv.org/abs/1506.02626
[github]: https://github.com/apache/incubator-singa
[24 Ultimate Data Scientists To Follow in the World Today]: http://www.analyticsvidhya.com/blog/2015/09/ultimate-data-scientists-world-today/
[Link 19]: http://blog.csdn.net/heyongluoyao8/article/details/48598169
[Deep Learning for NLP - Lecture October 2015]: https://github.com/nreimers/deeplearning4nlp-tutorial/tree/master/2015-10_Lecture/
[Connection between probability theory and real analysis]: https://ccle.ucla.edu/mod/page/view.php?id=834267
[Probability_ Theory and Examples]: http://www.math.duke.edu/~rtd/PTE/PTE4_1.pdf
[Link 20]: https://terrytao.wordpress.com/category/275a-probability-theory/
[Data Science Learning Resources]: http://www.districtdatalabs.com/#!resources/c21hq
[8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset]: http://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
[Top 20 Data Science MOOCs]: https://datarithms.wordpress.com/2015/08/16/top-20-data-science-moocs/
[Recurrent Neural Networks]: https://shapeofdata.wordpress.com/2015/10/20/recurrent-neural-networks/
[Histograms of Oriented Gradients]: http://www.cs.duke.edu/courses/fall15/compsci527/notes/hog.pdf
[Computational modelling courses]: http://aidanhorner.blogspot.co.uk/2015/10/computational-modelling-courses.html
[How We Use Deep Learning to Classify Business Photos at Yelp]: http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-to-classify-business-photos-at-yelp.html
[Link 21]: http://mp.weixin.qq.com/s?__biz=MzIxMjAzNDY5Mg==&mid=400067748&idx=1&sn=9c88eadfba5462281cd496e85ba3329c
[Link 22]: https://tigerneil.gitbooks.io/neural-networks-and-deep-learning-zh/content/
[Books to Read if You Might Be Interested in Data Science]: http://www.datasciguide.com/books-to-read-if-you-might-be-interested-in-data-science/
[Deep Learning for NLP resources]: https://github.com/andrewt3000/DL4NLP
[GitXiv]: http://gitxiv.com/
[Learning Multi-Domain Convolutional Neural Networks for Visual Tracking]: http://arxiv.org/pdf/1510.07945v1.pdf
[Beginners Guide_ Apache Spark Machine Learning Scenario With A Large Input Dataset]: http://fullstackml.com/2015/10/29/beginners-guide-apache-spark-machine-learning-scenario-with-a-large-input-dataset/
[Semantic Scholar]: https://www.semanticscholar.org/
[Semi-Supervised Learning]: http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf
[Xiaojin _Jerry_ Zhu]: http://pages.cs.wisc.edu/~jerryzhu/
[Introduction to Semi-Supervised Learning]: http://www.morganclaypool.com/doi/abs/10.2200/S00196ED1V01Y200906AIM006
[Free Resources for Beginners on Deep Learning and Neural Network]: http://www.analyticsvidhya.com/blog/2015/11/free-resources-beginners-deep-learning-neural-network/
[TensorFlow is an Open Source Software Library for Machine Intelligence]: http://tensorflow.org/
[white paper of tensorflow 2015]: http://pan.baidu.com/s/1jGyFPki
[hacker news]: https://news.ycombinator.com/item?id=10532957
[Google_TensorFlow]: https://www.youtube.com/watch?v=90-S1M7Ny_o&t=21m2s
[Veles_Distributed machine learning platform]: https://github.com/samsung/veles
[DMTK_Microsoft Distributed Machine Learning Tookit]: https://github.com/Microsoft/DMTK
[Semantics Approach to Big Data and Event Processing]: http://wiki.knoesis.org/index.php/BigDataTutorial
[LSTM_Long Short Term Memory_RNN_Recurrent]: http://www.zhihu.com/question/29411132
[Marvin_A minimalist GPU-only N-dimensional ConvNet framework]: http://marvin.is/
[Ufora is a compiled_ automatically parallel subset of python for data science and numerical computing]: http://ufora.github.io/ufora/
[Why I Open Sourced Five Years of Work]: https://medium.com/art-marketing/why-i-open-sourced-five-years-of-work-c5b5e0e38a6d
[Deep Learning and Deep Data Science - PyCon SE 2015]: https://www.youtube.com/watch?v=wBKfGaakFp8&hd=1
[Zhi-Hua Zhou Papers]: https://scholar.google.com/citations?user=rSVIHasAAAAJ&hl=zh-CN&oi=ao
[Advanced Linear Models for Data Science]: https://leanpub.com/lm
[Net2Net_ Accelerating Learning via Knowledge Transfer]: http://arxiv.org/abs/1511.05641
[Variational Inference]: https://www.youtube.com/playlist?list=PLFze15KrfxbF0n1zTNoFIaDpxnSyfgNgc
[Learning the Architecture of Deep Neural Networks]: http://arxiv.org/abs/1511.05497v1
[Multimodal Deep Learning]: http://ai.stanford.edu/~ang/papers/icml11-MultimodalDeepLearning.pdf
[TensorFlow_Torch_Theano_Mxnet]: http://chiffon.gitcafe.io/2015/11/16/long.html
[Mxnet]: https://github.com/dmlc/mxnet
[Notes Essays _CS183C_ Technology-enabled Blitzscaling_Stanford University]: https://medium.com/notes-essays-cs183c-technology-enabled-blitzscalin/latest
[Natural Language Understanding with Distributed Representation]: https://github.com/nyu-dl/NLP_DL_Lecture_Note
[Link 23]: http://arxiv.org/abs/1511.07916
[Recommender Systems Handbook]: http://link.springer.com/book/10.1007/978-1-4899-7637-6
[Understanding LSTM Networks]: http://colah.github.io/posts/2015-08-Understanding-LSTMs/index.html
[Link 24]: http://www.csdn.net/article/2015-11-25/2826323
[Machine Learning at Quora]: https://www.linkedin.com/pulse/machine-learning-quora-xavier-amatriain
[On Learning to Think_ Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models]: http://arxiv.org/abs/1511.09249
[The 5 Ways Data Scientists Keep Learning After College]: https://blog.rjmetrics.com/2015/12/01/the-5-ways-data-scientists-keep-learning-after-college/
[Deep Learning in Neural Networks_ An Overview]: http://arxiv.org/abs/1404.7828
[Contextual Learning]: http://arxiv.org/abs/1511.06429
[Link 25]: https://gitlab.tubit.tu-berlin.de/rbo-lab/concarne
[Machine Learning For Complete Beginners]: http://pythonforengineers.com/machine-learning-for-complete-beginners/
[Link 26]: https://github.com/shantnu/Titanic-Machine-Learning
[2015_CCF]: http://www.ccf.org.cn/sites/ccf/xhdtnry.jsp?contentId=2897719129810
[Learning to Hash Paper_ Code and Dataset]: http://cs.nju.edu.cn/lwj/L2H.html
[Neural networks with Theano and Lasagne]: https://www.youtube.com/watch?v=dtGhSE1PFh0
[github 1]: https://github.com/ebenolson/pydata2015
[Link 27]: http://vdisk.weibo.com/s/ayG13we2ltDAT
[Link 28]: http://weibo.com/xpqiu
[ppt]: http://vdisk.weibo.com/s/ayG13we2lDzcV
[Microsoft Open Sources Distributed Machine Learning Toolkit]: http://www.dmtk.io/index.html
[Link 29]: https://www.zhihu.com/question/20398418
[Michael I. Jordan]: http://www.cs.berkeley.edu/~jordan/
[Geoff Hinton]: http://www.cs.toronto.edu/~hinton/
[Yann Lecun]: http://yann.lecun.com/
[Yoshua Bengio]: http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html
[Yoshua Bengio 1]: http://www.infoq.com/cn/articles/ask-yoshua-bengio
[Yoshua Bengio 2]: http://www.infoq.com/cn/articles/ask-yoshua-bengio-2
[Large Scale Deep Learning within google]: http://static.googleusercontent.com/media/research.google.com/en/us/people/jeff/CIKM-keynote-Nov2014.pdf
[Deep Learning_ An MIT Press Book in Preparation]: http://goodfeli.github.io/dlbook/
[A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction]: http://arxiv.org/abs/1512.06293
[Microsoft Research Asia_Kaiming He]: http://research.microsoft.com/en-us/um/people/kahe/
[Speech and Language Processing _3rd ed. draft]: http://web.stanford.edu/~jurafsky/slp3/
[LSA 311_ Computational Lexical Semantics - Summer 2015]: http://web.stanford.edu/~jurafsky/li15/
[Link 30]: http://ocw.sjtu.edu.cn/G2S/OCW/cn/CourseDetails.htm?Id=397
[Link 31]: http://pan.baidu.com/s/1mgPi7jU
[Link 32]: http://ocw.sjtu.edu.cn/G2S/OCW/cn/CourseDetails.htm?Id=398
[Computational Linguistics and Deep Learning]: http://www.mitpressjournals.org/doi/pdf/10.1162/COLI_a_00239
[Link 33]: http://techtalks.tv/talks/computational-linguistics-and-deep-learning/61759/
[Deep Learning_ An Introduction from the NLP Perspective]: https://speakerdeck.com/baojie/deep-learning-an-introduction-from-the-nlp-perspective-by-kevin-duh
[Black Hat USA 2015 - Deep Learning On Disassembly]: https://www.youtube.com/watch?v=zfVfpMcUkq8
[https_www.blackhat.com_docs_us-15_materials_us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification.pdf_material_https_www.blackhat.com_docs_us-15_materials_us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification-wp.pdf]: https://www.blackhat.com/docs/us-15/materials/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification.pdf%29,%5Bmaterial%5D%28https://www.blackhat.com/docs/us-15/materials/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification-wp.pdf
[LibRec_A Java Library for Recommender Systems]: http://www.librec.net/
[Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection]: http://arxiv.org/abs/1512.07372
[Computational Statistics in Python]: http://people.duke.edu/~ccc14/sta-663/
[New open-source Machine Learning Framework written in Java]: http://blog.datumbox.com/new-open-source-machine-learning-framework-written-in-java/
[Awesome Recurrent Neural Networks]: http://jiwonkim.org/awesome-rnn/
[Pedro Domingos]: http://homes.cs.washington.edu/~pedrod/
[Link 34]: http://www.almosthuman.cn/2015/11/28/t8ysa/
[Video resources for machine learning]: http://dustintran.com/blog/video-resources-for-machine-learning/
[Deep Machine Learning libraries and frameworks]: https://medium.com/@abduljaleel/deep-machine-learning-libraries-and-frameworks-5fdf2bb6bfbe#.lwn2iyjsn
[Link 35]: https://github.com/Flowerowl/Big-Data-Resources
[Bayesian Methods in Astronomy_ Hands-on Statistics]: http://nbviewer.ipython.org/github/jakevdp/AAS227Workshop/blob/master/Index.ipynb
[Statistical Learning with Sparsity_ The Lasso and Generalizations]: http://web.stanford.edu/~hastie/StatLearnSparsity/index.html
[Trevor Hastie]: http://web.stanford.edu/~hastie/
[Rob Tibshirani]: http://statweb.stanford.edu/~tibs/
[The Evolution of Distributed Programming in R]: http://www.mango-solutions.com/wp/2016/01/the-evolution-of-distributed-programming-in-r/
[R]: https://aschinchon.wordpress.com/2016/01/07/climatic-change-at-a-glance/
[R 1]: http://blog.revolutionanalytics.com/2016/01/getting-started-with-markov-chains.html
[neon workshop at Startup.ML_ Sentiment Analysis and Deep Reinforcement Learning]: http://www.nervanasys.com/neon-workshop-at-startup-ml-sentiment-analysis-and-deep-reinforcement-learning/
[Startup.ML]: http://startup.ml/
[Understanding Convolution in Deep Learning]: http://timdettmers.com/2015/03/26/convolution-deep-learning/
[Python libraries for building recommender systems]: http://faroba.com/2015/12/03/a-python-libraries-for-building-recommender-systems/
[Neural networks class - Universit_ de Sherbrooke]: http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html
[CS231n_ Convolutional Neural Networks for Visual Recognition]: http://vision.stanford.edu/teaching/cs231n/index.html
[slides_video]: http://vision.stanford.edu/teaching/cs231n/syllabus.html
[homework]: http://cs231n.github.io/
[NIPS 2015 Deep Learning Symposium Part I]: http://yanran.li/peppypapers/2015/12/11/nips-2015-deep-learning-symposium-part-i.html
[Link 36]: http://yanran.li/peppypapers/2016/01/09/nips-2015-deep-learning-symposium-part-ii.html
[python]: http://michaelxiang.me/2015/12/16/python-machine-learning-list/
[Reading Text in the Wild with Convolutional Neural Networks]: http://www.robots.ox.ac.uk/~vgg/publications/2016/Jaderberg16/
[Yet Another Computer Vision Index To Datasets _YACVID]: http://riemenschneider.hayko.at/vision/dataset/
[Why SLAM Matters_ The Future of Real-Time SLAM_ and Deep Learning vs SLAM]: http://www.computervisionblog.com/2016/01/why-slam-matters-future-of-real-time.html
[Python based Deep Learning Framework by Nervana]: https://github.com/NervanaSystems/neon
[mageNet and MS COCO Visual Recognition Challenges video and slider]: http://image-net.org/challenges/ilsvrc+mscoco2015
[An Introduction to Machine Learning with Python]: http://blog.districtdatalabs.com/an-introduction-to-machine-learning-with-python
[Neural Enquirer_ Learning to Query Tables with Natural Language]: http://arxiv.org/abs/1512.00965
[Deep Learning - Taking machine learning to the next level]: https://www.udacity.com/course/deep-learning--ud730
[100 _must read_ R-bloggers_ posts for 2015]: http://www.r-bloggers.com/100-must-read-r-bloggers-posts-for-2015/
[Machine Learning_ a Probabilistic Perspective]: http://www.cs.ubc.ca/~murphyk/MLbook/index.html
[Undirected graphical models Markov random fields]: http://www.cs.ubc.ca/~murphyk/MLbook/pml-print3-ch19.pdf
[Yoshua Bengio_ How can one get started with machine learning]: https://www.quora.com/How-can-one-get-started-with-machine-learning-1
[UFLDL Recommended Readings]: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Recommended_Readings
[CSE 705_ Deep Learning _Spring 2015]: http://www.cse.buffalo.edu/~hungngo/classes/2015/705/
[Theano is a Deep learning Python library]: https://github.com/Theano/Theano
[Theano tutorial]: https://github.com/marekrei/theano-tutorial
[Document]: http://deeplearning.net/software/theano/tutorial/
[Statistical Language Models Based On Neural Networks]: http://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf
[Link 37]: http://www.infoq.com/cn/articles/machine-learning-automatic-classification-of-text-data
[Link 38]: http://www.infoq.com/cn/articles/machine-learning-automatic-classification-of-text-data-part2
[Pixel Recurrent Neural Networks]: http://arxiv.org/abs/1601.06759
[Computational Network Toolkit _CNTK]: https://github.com/Microsoft/CNTK
[An Introduction to Computational Networks and the Computational Network Toolkit]: http://research.microsoft.com/pubs/226641/CNTKBook-20160121.pdf
[Kalman and Bayesian Filters in Python]: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
[Statistical inference for data science]: https://leanpub.com/LittleInferenceBook
[GitHub]: https://github.com/bcaffo/LittleInferenceBook
[Learning Deep Architectures for AI]: http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf
[Machine Learning Tutorials]: https://github.com/ujjwalkarn/Machine-Learning-Tutorials
[Open Source Deep Learning Server]: http://www.deepdetect.com/
[github 2]: https://github.com/beniz/deepdetect
[Data Mining_ Analytics_ Big Data_ and Data Science]: http://www.kdnuggets.com/
[Data Mining And Statistics_ What_s The Connection]: http://docs.salford-systems.com/dm-stat.pdf
[NIPS_2015 Tutorial_Yoshua Bengio]: https://drive.google.com/file/d/0BxKBnD5y2M8NVnBpbWVwYUpQTjg/view
[Matt Might_Reading for graduate students]: http://matt.might.net/articles/books-papers-materials-for-graduate-students/
[Awesome Public Datasets]: https://github.com/caesar0301/awesome-public-datasets
[Introduction to Probability - The Science of Uncertainty]: https://www.edx.org/course/introduction-probability-science-mitx-6-041x-1
[R software and tools for everyday use]: http://xrds.acm.org/blog/2016/02/r-software-and-tools-for-everyday-use/
[Implementing Dynamic memory networks]: http://yerevann.github.io//2016/02/05/implementing-dynamic-memory-networks/
[Deeplearning4j 1]: http://deeplearning4j.org/zh-index.html/
[Big Data Analysis Learning Resources_ 50 Courses_ Blogs_ Tutorials_ And More For Mastering Big Data Analytics]: http://www.ngdata.com/big-data-analysis-resources/
[A Full Hardware Guide to Deep Learning]: http://timdettmers.com/2015/03/09/deep-learning-hardware-guide/
[Link 39]: http://www.almosthuman.cn/2016/02/04/bqrzz/
[Deep Residual Networks]: https://github.com/KaimingHe/deep-residual-networks
[The Definitive Guide to Natural Language Processing]: https://blog.monkeylearn.com/the-definitive-guide-to-natural-language-processing/
[Evaluating language identification performance]: https://blog.twitter.com/2015/evaluating-language-identification-performance
[ICLR 2016 Accepted Papers]: http://www.iclr.cc/doku.php?id=iclr2016:main&#accepted_papers_conference_track
[Machine Learning_ An In-Depth_ Non-Technical Guide - Part 1]: http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/
[Data Storytelling 101_ Helpful Tools for Gathering Ideas_ Designing Content _ More]: http://blog.hubspot.com/marketing/toolbox-for-data-storytelling
[WikiTableQuestions_ a Complex Real-World Question Understanding Dataset]: http://nlp.stanford.edu/blog/wikitablequestions-a-complex-real-world-question-understanding-dataset/
[Big Data_ 35 Brilliant And Free Data Sources For 2016]: http://www.forbes.com/sites/bernardmarr/2016/02/12/big-data-35-brilliant-and-free-data-sources-for-2016/#64ede4f16796
[SPARKNET_ training deep networks in spark]: http://arxiv.org/pdf/1511.06051v3.pdf
[github 3]: https://github.com/amplab/SparkNet
[DeepLearning.University _ An Annotated Deep Learning Bibliography _ Memkite]: http://memkite.com/deep-learning-bibliography/
[Learning Deep Learning]: http://rt.dgyblog.com/ref/ref-learning-deep-learning.html
[Awesome42 The easiest way to find R packages]: http://awesome42.com/
[MLbase_Distributed Machine Learning Made Easy]: http://mlbase.org/
[Prof. Dr. Tim Kraska]: http://cs.brown.edu/~kraskat/
[Deep Learning At Scale and At Ease]: http://www.comp.nus.edu.sg/~ooibc/singa-tomm.pdf
[SINGA]: http://singa.incubator.apache.org/index.html
[Learn All About Apache Spark _100x Faster than Hadoop MapReduce]: http://datasciencereport.com/2016/02/19/apache-spark/
[R For Deep Learning _I_ Build Fully Connected Neural Network From Scratch]: http://www.parallelr.com/r-deep-neural-network-from-scratch/
[A Visual Introduction to Machine Learning]: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
[Citation Network Dataset]: http://aminer.org/citation
[Best Free Machine Learning Ebooks]: https://www.reddit.com/r/MachineLearning/comments/47ast8/best_free_machine_learning_ebooks/
[International Conference on Computer Vision _ICCV_ 2015_ Santiago]: http://videolectures.net/iccv2015_santiago/
[CaffeOnSpark Open Sourced for Distributed Deep Learning on Big Data Clusters]: https://github.com/yahoo/CaffeOnSpark
[A Short Introduction to Learning to Rank]: http://research.microsoft.com/en-us/people/hangli/l2r.pdf
[Global Deep learning researcher]: https://aminer.org/search/t=b&q=Deep%20Learning
[Top Spark Ecosystem Projects]: http://www.kdnuggets.com/2016/03/top-spark-ecosystem-projects.html
[Spark]: http://www.infoq.com/cn/news/2016/03/spark-eco-project
[Proceedings of the 21st International Conference on Intelligent User Interfaces]: http://dl.acm.org/citation.cfm?id=2856767&preflayout=flat
[ACM IUI_16]: http://iui.acm.org/2016/
[Conference Navigator - Proceedings]: http://halley.exp.sis.pitt.edu/cn3/proceedingswithauthors.php?conferenceID=139
[2]: http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-2/
[3]: http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-3/
[4]: http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide-part-4/
[Oxford Deep Learning]: http://www.computervisiontalks.com/tag/deep-learning-course/
[Nando de Freitas]: https://www.cs.ox.ac.uk/people/nando.defreitas/
[youtube]: https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu
[computervisiontalks]: http://www.computervisiontalks.com/
[youtube 1]: https://www.youtube.com/channel/UC0z_jCi0XWqI8awUuQRFnyw
[Neural Networks for Machine Learning]: https://www.coursera.org/course/neuralnets
[Deep Learning News 1]: http://news.startup.ml/
[Maxout Networks]: http://jmlr.org/proceedings/papers/v28/goodfellow13.pdf
[Advances in Neural Information Processing Systems]: http://papers.nips.cc/
[Machine learning applications in genetics and genomics]: http://www.nature.com/nrg/journal/v16/n6/abs/nrg3920.html
[Link 40]: https://www.zhihu.com/question/41428117/answer/91045285
[Deep Learning in Bioinformatics]: http://arxiv.org/abs/1603.06430
[A Few Useful Things to Know about Machine Learning]: https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
[Cambridge Machine Learning Group]: http://mlg.eng.cam.ac.uk/
[Randy Olson_s data analysis and machine learning projects]: https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects
[Randy Olson_s]: http://www.randalolson.com/blog/
[GoLearn_Golang machine learning library]: https://github.com/sjwhitworth/golearn
[Swift Ai]: https://github.com/collinhundley/Swift-AI
[Please explain Support Vector Machines _SVM_ like I am a 5 year old]: https://www.reddit.com/r/MachineLearning/comments/15zrpp/please_explain_support_vector_machines_svm_like_i/
[reddit Machine learning]: https://www.reddit.com/r/MachineLearning/
[ComputerVision resource]: http://blog.csdn.net/carson2005/article/details/6601109
[Multimedia Laboratory Homepage]: http://mmlab.ie.cuhk.edu.hk/index.html
[2013_deep learning]: http://mmlab.ie.cuhk.edu.hk/project_deep_learning.html
[Search Engines that Learn from Their Users]: http://www.anneschuth.nl/wp-content/uploads/thesis_anne-schuth_search-engines-that-learn-from-their-users.pdf
[Deep Learning Books]: http://machinelearningmastery.com/deep-learning-books/
[Towards Bayesian Deep Learning_ A Survey]: http://arxiv.org/abs/1604.01662
[Revisiting Distributed Synchronous SGD]: http://arxiv.org/abs/1604.00981
[Link 41]: http://wxwidget.github.io/blog/2014/08/17/large-scale-deep-network/
[Research Issues in Social Computing]: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.102.6931&rep=rep1&type=pdf
[What are some important areas of research in social computing right now]: https://www.quora.com/What-are-some-important-areas-of-research-in-social-computing-right-now
[Collaborative Filtering Recommender Systems]: http://files.grouplens.org/papers/FnT%20CF%20Recsys%20Survey.pdf
[Content-Boosted Collaborative Filtering for Improved Recommendations]: http://www.cs.utexas.edu/~ml/papers/cbcf-aaai-02.pdf
[Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion]: http://siplab.tudelft.nl/sites/default/files/sigir06_similarityfusion.pdf
[Item-based Collaborative Filtering Recommendation Algorithms]: http://glaros.dtc.umn.edu/gkhome/fetch/papers/www10_sarwar.pdf
[Amazon.com Recommendations Item-to-Item Collaborative Filtering]: http://www.cin.ufpe.br/~idal/rs/Amazon-Recommendations.pdf
[Collaborative Filtering for Implicit Feedback Datasets]: http://yifanhu.net/PUB/cf.pdf
[Tutorials_ papers and code for computer graphics_ fractals and demoscene]: http://www.iquilezles.org/www/index.htm
[ELEN 6886 Sparse Representation and High-Dimensional Geometry]: http://www.columbia.edu/~jw2966/6886_Fa2015.html
[Link 42]: http://www.columbia.edu/~jw2966/papers/WYGSM09-PAMI.pdf
[Software engineer how to learning Machine learning]: https://www.quora.com/What-would-be-your-advice-to-a-software-engineer-who-wants-to-learn-machine-learning-3/answer/Alex-Smola-1
[Book review_ Fundamentals of Deep Learning]: http://www.opengardensblog.futuretext.com/archives/2015/08/book-review-fundamentals-of-deep-learning-designing-next-generation-artificial-intelligence-algorithms-by-nikhil-buduma.html
[Link 43]: https://github.com/darksigma/Fundamentals-of-Deep-Learning-Book
[Learning from Big Code]: http://learnbigcode.github.io/
[Object Detection]: http://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
[Deep Learning_ Course by Yann LeCun at Coll_ge de France 2016_Slides in English]: https://www.facebook.com/yann.lecun/posts/10153505343037143
[Link 44]: http://pan.baidu.com/s/1jIIrljg
[Link 45]: https://drive.google.com/folderview?id=0BxKBnD5y2M8NclFWSXNxa0JlZTg&usp=sharing
[Stanford HCI Group]: http://hci.stanford.edu/
[Learn Data Science]: https://github.com/nborwankar/LearnDataScience
[CS224D Lecture 7 - Introduction to TensorFlow]: https://www.youtube.com/watch?v=L8Y2_Cq2X5s
[CS224D]: http://cs224d.stanford.edu/
[ppt 1]: http://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf
[DeepDreaming with TensorFlow]: http://nbviewer.jupyter.org/github/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb
[Leaf - Machine Learning for Hackers]: http://autumnai.com/leaf/book/leaf.html
[Leaf]: https://github.com/autumnai/leaf
[MXnet_Flexible and Efficient library for deep learning]: http://on-demand.gputechconf.com/gtc/2016/video/S6853.html
[GTC 2016]: http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.php
[MXNet Tutorial for NVidia GTC 2016.]: https://github.com/dmlc/mxnet-gtc-tutorial
[OpenAI Gym_ Toolkit for developing_ comparing reinforcement learning algorithms]: https://gym.openai.com/
[conference-iclr-2016 Papers and Code]: https://tensortalk.com/?cat=conference-iclr-2016
[probabilistic graphical models principles and techniques]: https://github.com/JimmyLin192/GraphicalModel/blob/master/Probabilistic%20Graphical%20Models%20Principles%20and%20Techniques.pdf
[Link 46]: https://vk.com/doc168073_304660839?hash=39a33dd8aa6b141d8a&dl=b667454bc650f66cc0
[Information Theory_ Inference_ and Learning Algorithms]: http://www.inference.phy.cam.ac.uk/itprnn/book.html
[Convex Optimization _ Boyd and Vandenberghe]: http://stanford.edu/~boyd/cvxbook/
[Machine - Learning - Tom Mitchell]: http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf
[Learning with Kernels Support Vector Machines_ Regularization_ Optimization_ and Beyond]: https://mitpress.mit.edu/books/learning-kernels
[Learning with Kernels PPT]: http://dip.sun.ac.za/~hanno/tw796/lesings/mlss06au_scholkopf_lk.pdf
[Learning with Kernels]: https://www.cs.utah.edu/~piyush/teaching/learning-with-kernels.pdf
[The Elements of Statistical Learning_ Data Mining_ Inference_ and Prediction]: http://statweb.stanford.edu/~tibs/ElemStatLearn/
[Data Mining_ Practical Machine Learning Tools and Techniques]: http://www.cse.hcmut.edu.vn/~chauvtn/data_mining/Texts/%5B7%5D%20Data%20Mining%20-%20Practical%20Machine%20Learning%20Tools%20and%20Techniques%20%283rd%20Ed%29.pdf
[Foundations of Statistical Natural Language Processing]: http://nlp.stanford.edu/fsnlp/
[Speech and Language Processing]: https://web.stanford.edu/~jurafsky/slp3/
[Natural Language Processing with Python - NLTK]: http://www.nltk.org/book/
[https_www.cnblogs.com_charlotte77_p_5485438.html]: https://www.cnblogs.com/charlotte77/p/5485438.html