浏览代码

Update README.md

Ashara12 10 年之前
父节点
当前提交
8fcfd7de13
共有 1 个文件被更改,包括 78 次插入45 次删除
  1. 78 45
      README.md

+ 78 - 45
README.md

@@ -1,60 +1,93 @@
+<div class="page-content">
+      <div class="wrapper">
+        <div class="post">
+
+  <header class="post-header">
+
+# Deep learning Reading List
+
+  </header>
+
+  <article class="post-content">
+
+Following is a growing list of some of the materials i found on the web for Deep Learning beginners. 
+
 ### Free Online Books
 
-1.  [Deep Learning][] by Yoshua Bengio, Ian Goodfellow and Aaron
-    Courville
-2.  [Neural Networks and Deep Learning][] by Michael Nielsen
-3.  [Deep Learning][1] by Microsoft Research
-4.  [Deep Learning Tutorial][] by LISA lab, University of Montreal
+1.  [Deep Learning](http://www.iro.umontreal.ca/~bengioy/dlbook/) by Yoshua Bengio, Ian Goodfellow and Aaron Courville
+2.  [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by  Michael Nielsen
+3.  [Deep Learning](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf) by Microsoft Research
+4.  [Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) by LISA lab, University of Montreal
 
 ### Courses
 
-1.  [Machine Learning][] by Andrew Ng in Coursera
-2.  [Neural Networks for Machine Learning][] by Geoffrey Hinton in
-    Coursera
-3.  [Neural networks class][] by Hugo Larochelle from Université de
-    Sherbrooke
-4.  [Deep Learning Course][] by CILVR lab @ NYU
+1.  [Machine Learning](https://class.coursera.org/ml-005) by Andrew Ng in Coursera
+2.  [Neural Networks for Machine Learning](https://class.coursera.org/neuralnets-2012-001) by Geoffrey Hinton in Coursera
+3.  [Neural networks class](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH) by Hugo Larochelle from Université de Sherbrooke
+4.  [Deep Learning Course](http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start) by CILVR lab @ NYU
 
 ### Video and Lectures
 
-1.  [How To Create A Mind][] By Ray Kurzweil - Is a inspiring talk
-2.  [Deep Learning, Self-Taught Learning and Unsupervised Feature
-    Learning][] By Andrew Ng
-3.  [Recent Developments in Deep Learning][] 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
-    Lab][] by Adam Coates
-8.  [Making Sense of the World with Deep Learning][] By Adam Coates
-9.  [Demystifying Unsupervised Feature Learning][] By Adam Coates
-10. [Visual Perception with Deep Learning][] By Yann LeCun
+1.  [How To Create A Mind](https://www.youtube.com/watch?v=RIkxVci-R4k) By Ray Kurzweil - Is a inspiring talk2.  [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning](https://www.youtube.com/watch?v=n1ViNeWhC24) By Andrew Ng
+3.  [Recent Developments in Deep Learning](https://www.youtube.com/watch?v=vShMxxqtDDs&amp;index=3&amp;list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) By Geoff Hinton
+4.  [The Unreasonable Effectiveness of Deep Learning](https://www.youtube.com/watch?v=sc-KbuZqGkI) by Yann LeCun
+5.  [Deep Learning of Representations](https://www.youtube.com/watch?v=4xsVFLnHC_0) by Yoshua bengio
+6.  [Principles of Hierarchical Temporal Memory](https://www.youtube.com/watch?v=6ufPpZDmPKA) by Jeff Hawkins
+7.  [Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab](https://www.youtube.com/watch?v=2QJi0ArLq7s&amp;list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT) by Adam Coates
+8.  [Making Sense of the World with Deep Learning](http://vimeo.com/80821560) By Adam Coates
+9.  [Demystifying Unsupervised Feature Learning ](https://www.youtube.com/watch?v=wZfVBwOO0-k) By Adam Coates
+10.  [Visual Perception with Deep Learning](https://www.youtube.com/watch?v=3boKlkPBckA) By Yann LeCun
 
 ### Papers
 
-</div>
+1.  [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
+2.  [Using Very Deep Autoencoders for Content Based Image Retrieval](http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf)
+3.  [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf)
+4.  [CMU’s list of papers](http://deeplearning.cs.cmu.edu/)
 
-</div>
+### Tutorials
+
+1.  [UFLDL Tutorial 1](http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial)
+2.  [UFLDL Tutorial 2](http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/)
+3.  [Deep Learning for NLP (without Magic)](http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial)
+4.  [A Deep Learning Tutorial: From Perceptrons to Deep Networks](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks)
+
+### WebSites
+
+1.  [deeplearning.net](http://deeplearning.net/)
+2.  [deeplearning.stanford.edu](http://deeplearning.stanford.edu/)
+
+### Datasets
+
+1.  [MNIST](http://yann.lecun.com/exdb/mnist/) Handwritten digits
+2.  [Google House Numbers](http://ufldl.stanford.edu/housenumbers/) from street view
+3.  [CIFAR-10 and CIFAR-100](http://www.cs.toronto.edu/~kriz/cifar.html)4.  [IMAGENET](http://www.image-net.org/)
+5.  [Tiny Images](http://groups.csail.mit.edu/vision/TinyImages/) 80 Million tiny images6.  [Flickr Data](http://yahoolabs.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images) 100 Million Yahoo dataset
+7.  [Berkeley Segmentation Dataset 500](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)
+
+### Frameworks
+
+1.  [Caffe](http://caffe.berkeleyvision.org/)2.  [Torch7](http://torch.ch/)
+3.  [Theano](http://deeplearning.net/software/theano/)
+4.  [cuda-convnet](https://code.google.com/p/cuda-convnet2/)5.  [Ccv](http://libccv.org/doc/doc-convnet/)
+6.  [NuPIC](http://numenta.org/nupic.html)
+7.  [DeepLearning4J](http://deeplearning4j.org/)
+
+### Miscellaneous
+
+1.  [Google Plus - Deep Learning Community](https://plus.google.com/communities/112866381580457264725)
+2.  [Caffe Webinar](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=+)
+3.  [100 Best Github Resources in Github for DL](http://meta-guide.com/software-meta-guide/100-best-github-deep-learning/)
+4.  [Word2Vec](https://code.google.com/p/word2vec/)
+5.  [Caffe DockerFile](https://registry.hub.docker.com/u/tleyden5iwx/caffe/)
+6.  [TorontoDeepLEarning convnet](https://github.com/TorontoDeepLearning/convnet)
+7.  [Vision data sets](http://www.cs.cmu.edu/~cil/v-images.html)
+8.  [Fantastic Torch Tutorial](http://code.cogbits.com/wiki/doku.php) My personal favourite. Also check out [gfx.js](https://github.com/clementfarabet/gfx.js)
+9.  [Torch7 Cheat sheet](https://github.com/torch/torch7/wiki/Cheatsheet)
+
+  </article>
 
 </div>
 
-  [Deep Learning]: http://www.iro.umontreal.ca/~bengioy/dlbook/
-  [Neural Networks and Deep Learning]: http://neuralnetworksanddeeplearning.com/
-  [1]: http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf
-  [Deep Learning Tutorial]: http://deeplearning.net/tutorial/deeplearning.pdf
-  [Machine Learning]: https://class.coursera.org/ml-005
-  [Neural Networks for Machine Learning]: https://class.coursera.org/neuralnets-2012-001
-  [Neural networks class]: https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
-  [Deep Learning Course]: http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start
-  [How To Create A Mind]: https://www.youtube.com/watch?v=RIkxVci-R4k
-  [Deep Learning, Self-Taught Learning and Unsupervised Feature
-  Learning]: https://www.youtube.com/watch?v=n1ViNeWhC24
-  [Recent Developments in Deep Learning]: https://www.youtube.com/watch?v=vShMxxqtDDs&index=3&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
-  Lab]: https://www.youtube.com/watch?v=2QJi0ArLq7s&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT
-  [Making Sense of the World with Deep Learning]: http://vimeo.com/80821560
-  [Demystifying Unsupervised Feature Learning]: https://www.youtube.com/watch?v=wZfVBwOO0-k
-  [Visual Perception with Deep Learning]: https://www.youtube.com/watch?v=3boKlkPBckA
+      </div>
+    </div>