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Title: GitHub - DeepCompute/deep-learning-study: 深度学习相关资料 · GitHub

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https://github.com/DeepCompute/deep-learning-study#deep-learning
https://github.com/DeepCompute/deep-learning-study#free-online-books
Deep Learninghttp://www.iro.umontreal.ca/~bengioy/dlbook/
Neural Networks and Deep Learninghttp://neuralnetworksanddeeplearning.com/
Deep Learninghttp://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf
Deep Learning Tutorialhttp://deeplearning.net/tutorial/deeplearning.pdf
https://github.com/DeepCompute/deep-learning-study#courses
Machine Learninghttps://class.coursera.org/ml-005
Neural Networkshttps://class.coursera.org/neuralnets-2012-001
Neural networks classhttps://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH
Deep Learning Coursehttp://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start
https://github.com/DeepCompute/deep-learning-study#video-and-lectures
How To Create A Mindhttps://www.youtube.com/watch?v=RIkxVci-R4k
Deep Learning, Self-Taught Learning and Unsupervised Feature Learninghttps://www.youtube.com/watch?v=n1ViNeWhC24
Recent Developments in Deep Learninghttps://www.youtube.com/watch?v=vShMxxqtDDs&index=3&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT
The Unreasonable Effectiveness of Deep Learninghttps://www.youtube.com/watch?v=sc-KbuZqGkI
Deep Learning of Representationshttps://www.youtube.com/watch?v=4xsVFLnHC_0
Principles of Hierarchical Temporal Memoryhttps://www.youtube.com/watch?v=6ufPpZDmPKA
Machine Learning Discussion Group - Deep Learning w/ Stanford AI Labhttps://www.youtube.com/watch?v=2QJi0ArLq7s&list=PL78U8qQHXgrhP9aZraxTT5-X1RccTcUYT
Making Sense of the World with Deep Learninghttp://vimeo.com/80821560
Demystifying Unsupervised Feature Learninghttps://www.youtube.com/watch?v=wZfVBwOO0-k
Visual Perception with Deep Learninghttps://www.youtube.com/watch?v=3boKlkPBckA
https://github.com/DeepCompute/deep-learning-study#papers
ImageNet Classification with Deep Convolutional Neural Networkshttp://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
Using Very Deep Autoencoders for Content Based Image Retrievalhttp://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf
Learning Deep Architectures for AIhttp://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf
CMU’s list of papershttp://deeplearning.cs.cmu.edu/
https://github.com/DeepCompute/deep-learning-study#tutorials
UFLDL Tutorial 1http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial
UFLDL Tutorial 2http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/
Deep Learning for NLPhttp://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
A Deep Learning Tutorial: From Perceptrons to Deep Networkshttp://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks
https://github.com/DeepCompute/deep-learning-study#websites
deeplearning.nethttp://deeplearning.net/
deeplearning.stanford.eduhttp://deeplearning.stanford.edu/
https://github.com/DeepCompute/deep-learning-study#datasets
MNIST Handwritten digitshttp://yann.lecun.com/exdb/mnist/
Google House Numbers from street viewhttp://ufldl.stanford.edu/housenumbers/
CIFAR-10 and CIFAR-100http://www.cs.toronto.edu/~kriz/cifar.html
IMAGENEThttp://www.image-net.org/
Tiny Images 80 Million tiny imageshttp://groups.csail.mit.edu/vision/TinyImages/
Flickr Data 100 Million Yahoo datasethttp://yahoolabs.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images
Berkeley Segmentation Dataset 500http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
https://github.com/DeepCompute/deep-learning-study#frameworks
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100 Best Github Resources in Github for DLhttp://meta-guide.com/software-meta-guide/100-best-github-deep-learning/
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