deep learning入门教材

最近打算系统的学习下deep learning。总结了下经典的入门材料：

1. 文章Survey：参见 http://deeplearning.net/reading-list/
Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent,
Arxiv, 2012.
The monograph or review paper Learning Deep Architectures for
AI (Foundations & Trends in Machine Learning, 2009).
Deep Machine Learning – A New Frontier in Artificial Intelligence Research – a survey
paper by Itamar Arel, Derek C. Rose, and Thomas P. Karnowski.
Graves, A. (2012). Supervised sequence labelling with recurrent neural networks(Vol. 385). Springer.
Schmidhuber, J. (2014). Deep Learning in Neural Networks: An Overview. 75 pages, 850+ references, http://arxiv.org/abs/1404.7828,
PDF & LATEX source & complete public BIBTEX file under http://www.idsia.ch/~juergen/deep-learning-overview.html.

2. 最近学术界的研究进展(cvpr 2014 tutorial)
https://sites.google.com/site/deeplearningcvpr2014/， 阅读了部分材料， 这里面的介绍比较系统和前沿，读起来也不太难理解，有空详细阅读下reference。

3. 视频介绍入门：

hinton 在coursera的neutral network

Hugo 的视频https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH

4. tools：

https://github.com/torch, 工业界用的多，用lua，速度快，容易嵌入c，

http://deeplearning.net/software/pylearn2/， python易编程。

比较： http://arxiv.org/pdf/1308.4214.pdf
5. 入门教程：http://ufldl.stanford.edu/wiki/index.php，　很赞，　Ｎｇ的教程特别容易理解。