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转自：http://baojie.org/blog/2013/01/27/deep-learning-tutorials/
几个不错的深度学习教程，基本都有视频和演讲稿。附两篇综述文章和一副漫画。还有一些以后补充。
Jeff Dean 2013 @ Stanford
http://i.stanford.edu/infoseminar/dean.pdf
一个对DL能干什么的入门级介绍，主要涉及Google在语音识别、图像处理和自然语言处理三个方向上的一些应用。参《Spanner and Deep Learning》（2013-01-19）
Hinton 2009
A tutorial on Deep Learning
Slideshttp://videolectures.net/site/normal_dl/tag=52790/jul09_hinton_deeplearn.pdf
Video http://videolectures.net/jul09_hinton_deeplearn/ (3 hours)
从神经网络的背景来分析DL，为什么要有DL说得很清楚。对DL的基本模型结构也说得很清楚。十分推荐
更多Hinton的教程 http://www.cs.toronto.edu/~hinton/nntut.html
斯坦福的Deep Learning公开课（2012）
Samy Bengio, Tom Dean and Andrew Ng
http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=DeepLearning
教学语言是Matlab。
参2011年的课程CS294A/CS294W Deep Learning and Unsupervised Feature Learning
更多的斯坦福工作： Deep Learning in Natural Language Processing
NIPS 2009 tutorial
Deep Learning for Natural Language Processing, 2009 tutorial by Ronan Collobert (senna author)
这个介绍了DL在三个方向上的应用：tagging (parsing), semantic search, concept labeling
Ronan Collobert的Senna是一个c的深度学习实现，只有2000多行代码
ACL 2012 tutorial
Deep Learning for NLP (without Magic)
Video: http://www.youtube.com/watch?v=IF5tGEgRCTQ&list=PL4617D0E28A5781B0
Kai Yu’s Tutorial
On November 26, 2012
Title: “A Tutorial on Deep Learning”
In the past 30 years, tremendous progress has been made in building effective classification models. Despite the success, we have to realize that, in major AI challenges, the key bottleneck is not the quality of classifiers but that of features. Since 2006, learning high-level features using deep architectures has become a big wave of new learning paradigms. In recent two years, performance breakthrough was reported in both image and speech recognition tasks, indicating deep learning are not something ignorable. In this talk, I will walk through the recent works and key building blocks, e.g., sparse coding, RBMs, auto-encoders, etc. and list the major research topics, including modeling and computational issues. In the end, I will discuss what might be interesting topics for future research.
余 凯任百度技术副总监，多媒体部负责人，主要负责公司在语音，图像，音频等领域面向互联网和移动应用的技术研发。加盟百度前，余凯博士在美国NEC研究院担 任Media Analytics部门主管(Department Head)，领导团队在机器学习、图像识别、多媒体检索、视频监控，以及数据挖掘和人机交互等方面的产品技术研发。此前他曾在西门子公司任Senior Research Scientist。2011年曾在斯坦福大学计算机系客座主讲课程“CS121: 人工智能概论”。他在NIPS, ICML, CVPR, ICCV, ECCV，SIGIR, SIGKDD，TPAMI，TKDE等会议和杂志上发表了70多篇论文，H-index=28，曾担任机器学习国际会议ICML10, ICML11, NIPS11, NIPS12的Area Chair. 2012年他被评为中关村高端领军人才和北京市海聚计划高层次海外人才。
Slides link: http://pan.baidu.com/share/link?shareid=136269&uk=2267174042[1]
Video link: KaiYu_report.mp4 (519.2 MB)
Theano Deep Learning Tutorial
这个是实战, 如何用Python实现深度学习
http://deeplearning.net/tutorial/
Survey Papers
很多，不过初学看这两篇应该就够了
Yoshua Bengio, Aaron Courville, Pascal Vincent. (2012) Representation Learning: A Review and New Perspectives
Yoshua Bengio (2009). Learning Deep Architectures for AI.
更多
最后来个漫画
Deep Learning虽好，也要牢记它的局限
深度学习的一些教程
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原文地址：http://www.cnblogs.com/ydxt/p/5358191.html
