畢設做的是DBNs的相關研究，翻過一些資料，在此做個彙總。

可以通過谷歌學術搜尋來下載這些論文。

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.

深度學習的介紹性文章，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.

深度學習的經典論文，集大成者。可以當作深度學習的學習材料。

Hinton, G. E. Learning multiple layers of representation. Trends in Cognitive Sciences, vol. 11, pp. 428-434, 2007.

不需要太多數學知識即可掌握DBNs的關鍵演算法。這篇論文語言淺白，篇幅短小，適合初學者理解DBNs。

Hinton, G. E. To recognize shapes, first learn to generate images. Technical Report UTML TR 2006-003, University of Toronto, 2006.

多倫多大學的內部講義。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，作者詳細闡述了DBNs的方方面面，論證了其和一組層疊的RBMs的等價性，然後引出DBNs的學習演算法。

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里程碑，它標誌著深度學習總算有了高效的可行的演算法。

Hinton, G. E. A practical guide to training restricted boltzmann machines. Technical Report UTML TR 2010-003, University of Toronto, 2010.

一份訓練RBM的最佳實踐。

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.

闡述了非監督預訓練的作用。這兩篇可以結合起來一起看。

這篇部落格給出的材料更加全面，作者來自復旦大學，現似乎是在Yahoo Labs北京研究院工作。