learning

learning

关注
发信
关注(28)粉丝(399)

【深度学习Deep Learning】资料大全

　　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.  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

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 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 by Adam Coates
  8. Making Sense of the World with Deep Learning1 By Adam Coates
  9. Demystifying Unsupervised Feature LearningBy Adam Coates
  10. Visual Perception with Deep Learning3 By Yann LeCun
  11. Oxford Deep Learning -Nando de Freitas：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
  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
  4. 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-10034.  IMAGENET1
  4. Tiny Images1 80 Million tiny images6.  Flickr Data 100 Million Yahoo dataset
  5. Berkeley Segmentation Dataset 500

Frameworks

  1. Caffe92.  Torch73
  2. Theano3
  3. cuda-convnet25.  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  ：一个简单的CNN实现（Python）

几个常见应用领域

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）

 
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)（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）
 
 
 
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
 
 
04-15 07:34
Powered by LMLPHP ©2024 learning 0.093444