轉(zhuǎn)自：http://baojie.org/blog/2013/01/27/deep-learning-tutorials/

幾個(gè)不錯(cuò)的深度學(xué)習(xí)教程，基本都有視頻和演講稿。附兩篇綜述文章和一副漫畫。還有一些以后補(bǔ)充。

Jeff Dean 2013 @ Stanford

http://i.stanford.edu/infoseminar/dean.pdf

一個(gè)對(duì)DL能干什么的入門級(jí)介紹，主要涉及Google在語音識(shí)別、圖像處理和自然語言處理三個(gè)方向上的一些應(yīng)用。參《Spanner and Deep Learning》（2013-01-19）

Hinton 2009

A tutorial on Deep Learning

Slides http://videolectures.net/site/normal_dl/tag=52790/jul09_hinton_deeplearn.pdf

Video http://videolectures.net/jul09_hinton_deeplearn/? (3 hours)

從神經(jīng)網(wǎng)絡(luò)的背景來分析DL，為什么要有DL說得很清楚。對(duì)DL的基本模型結(jié)構(gòu)也說得很清楚。十分推薦

更多Hinton的教程 http://www.cs.toronto.edu/~hinton/nntut.html

斯坦福的Deep Learning公開課（2012）

Samy Bengio, Tom Dean and Andrew Ng

http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=DeepLearning

教學(xué)語言是Matlab。

參2011年的課程CS294A/CS294W? Deep Learning and Unsupervised Feature Learning

• UFLDL Tutorial Wiki:?Unsupervised Feature Learning and Deep Learning Tutorial?不長(zhǎng)，易懂

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)?

• http://ronan.collobert.com/pub/matos/2009_tutorial_nips.pdf
• video http://videolectures.net/nips09_collobert_weston_dlnl/

這個(gè)介紹了DL在三個(gè)方向上的應(yīng)用：tagging (parsing), semantic search, concept labeling

Ronan Collobert的Senna是一個(gè)c的深度學(xué)習(xí)實(shí)現(xiàn)，只有2000多行代碼

ACL 2012 tutorial

Deep Learning for NLP (without Magic)?

http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial? by Richard Socher, Yoshua Bengio and Chris Manning? PDF: http://nlp.stanford.edu/~socherr/SocherBengioManning-DeepLearning-ACL2012-20120707-NoMargin.pdf?

Video: http://www.youtube.com/watch?v=IF5tGEgRCTQ&list=PL4617D0E28A5781B0

Kai Yu’s Tutorial

http://vipl.ict.ac.cn/News/academic-report-tutorial-deep-learning-dr-kai-yu? On November 26, 2012 Title: “A Tutorial on Deep Learning”? Abstract:? 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.? Bio of Dr. Kai Yu:? 余 凱任百度技術(shù)副總監(jiān)，多媒體部負(fù)責(zé)人，主要負(fù)責(zé)公司在語音，圖像，音頻等領(lǐng)域面向互聯(lián)網(wǎng)和移動(dòng)應(yīng)用的技術(shù)研發(fā)。加盟百度前，余凱博士在美國(guó)NEC研究院擔(dān) 任Media Analytics部門主管(Department Head)，領(lǐng)導(dǎo)團(tuán)隊(duì)在機(jī)器學(xué)習(xí)、圖像識(shí)別、多媒體檢索、視頻監(jiān)控，以及數(shù)據(jù)挖掘和人機(jī)交互等方面的產(chǎn)品技術(shù)研發(fā)。此前他曾在西門子公司任Senior Research Scientist。2011年曾在斯坦福大學(xué)計(jì)算機(jī)系客座主講課程“CS121: 人工智能概論”。他在NIPS, ICML, CVPR, ICCV, ECCV，SIGIR, SIGKDD，TPAMI，TKDE等會(huì)議和雜志上發(fā)表了70多篇論文，H-index=28，曾擔(dān)任機(jī)器學(xué)習(xí)國(guó)際會(huì)議ICML10, ICML11, NIPS11, NIPS12的Area Chair. 2012年他被評(píng)為中關(guān)村高端領(lǐng)軍人才和北京市海聚計(jì)劃高層次海外人才。?

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

這個(gè)是實(shí)戰(zhàn), 如何用Python實(shí)現(xiàn)深度學(xué)習(xí)

http://deeplearning.net/tutorial/

Code https://github.com/lisa-lab/DeepLearningTutorials?

?

Survey Papers

很多，不過初學(xué)看這兩篇應(yīng)該就夠了

Yoshua Bengio, Aaron Courville, Pascal Vincent. (2012) Representation Learning: A Review and New Perspectives

Yoshua Bengio?(2009). Learning Deep Architectures for AI.

更多

• Itamar Arel, Derek C. Rose, and Thomas P. Karnowski. (2010)?Deep Machine Learning – A New Frontier in Artificial Intelligence Research ?這篇沒什么公式，也不長(zhǎng)，就是籠統(tǒng)的介紹一下
• 截至2009的一些重要文章 http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/ReadingOnDeepNetworks

最后來個(gè)漫畫

Deep Learning雖好，也要牢記它的局限

轉(zhuǎn)載于:https://www.cnblogs.com/retrieval/p/3209951.html

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