轉(zhuǎn)載 ： http://fantasticinblur.iteye.com/blog/1131640

Arel, I., Rose, D. C. and K arnowski, T. P. Deep machine learning - a new frontier in artificial intelligence research. Computational Intelligence Magazine, IEEE, vol. 5, pp. 13-18, 2010.
深度學(xué)習(xí)的介紹性文章，可做入門材料。

Bengio, Y. Learning deep architecture for AI. Foundations and Trends in Machine Learning, vol. 2, pp: 1-127, 2009.
深度學(xué)習(xí)的經(jīng)典論文，集大成者。可以當(dāng)作深度學(xué)習(xí)的學(xué)習(xí)材料。

Hinton, G. E. Learning multiple layers of representation. Trends in Cognitive Sciences, vol. 11, pp. 428-434, 2007.
不需要太多數(shù)學(xué)知識(shí)即可掌握DBNs的關(guān)鍵算法。這篇論文語言淺白，篇幅短小，適合初學(xué)者理解DBNs。

Hinton, G. E. To recognize shapes, first learn to generate images. Technical Report UTML TR 2006-003, University of Toronto, 2006.
多倫多大學(xué)的內(nèi)部講義。推薦閱讀。

Hinton, G. E., Osindero, S. and Teh, Y. W. A fast learning algorithm for deep belief nets. Neural Computation, vol 18, pp. 1527-1554, 2006.
DBNs的開山之作，意義非凡，一定要好好看幾遍。在這篇論文中，作者詳細(xì)闡述了DBNs的方方面面，論證了其和一組層疊的RBMs的等價(jià)性，然后引出DBNs的學(xué)習(xí)算法。

Hinton, G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science, vol. 313, no. 5786, pp. 504–507, 2006.
Science上的大作。這篇論文可是算作一個(gè)里程碑，它標(biāo)志著深度學(xué)習(xí)總算有了高效的可行的算法。

Hinton, G. E. A practical guide to training restricted boltzmann machines. Technical Report UTML TR 2010-003, University of Toronto, 2010.
一份訓(xùn)練RBM的最佳實(shí)踐。

Erhan, D., Manzagol, P. A., Bengio, Y., Bengio, S. and Vincent, P. The difficulty of training deep architectures and the effect of unsupervised pretraining. In The Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 153–160, 2009.

Erhan, D., Courville, A., Bengio, Y. and Vincent, P. Why Does Unsupervised Pre-training Help Deep Learning? In the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), Chia Laguna Resort, Sardinia, Italy, 2010.
闡述了非監(jiān)督預(yù)訓(xùn)練的作用。這兩篇可以結(jié)合起來一起看。

這篇博客給出的材料更加全面，作者來自復(fù)旦大學(xué)，現(xiàn)似乎是在Yahoo Labs北京研究院工作。
http://demonstrate.ycool.com/post.3006074.html

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