Deep Learning(4)

四、拓展学习推荐

Deep Learning 经典阅读材料：

• The monograph or review paper Learning Deep Architectures for AI (Foundations & Trends in Machine Learning, 2009).
• The ICML 2009 Workshop on Learning Feature Hierarchies webpage has a list of references.
• The LISA public wiki has a reading list and a bibliography.
• Geoff Hinton has readings from last year’s NIPS tutorial.

Deep Learning工具—— Theano：

Theano是deep learning的Python库，要求首先熟悉Python语言和numpy，建议读者先看Theano basic tutorial，然后按照Getting Started 下载相关数据并用gradient descent的方法进行学习。

学习了Theano的基本方法后，可以练习写以下几个算法：

有监督学习：

1. Logistic Regression - using Theano for something simple
2. Multilayer perceptron - introduction to layers
3. Deep Convolutional Network - a simplified version of LeNet5

无监督学习：

• Auto Encoders, Denoising Autoencoders - description of autoencoders
• Stacked Denoising Auto-Encoders - easy steps into unsupervised pre-training for deep nets
• Restricted Boltzmann Machines - single layer generative RBM model
• Deep Belief Networks - unsupervised generative pre-training of stacked RBMs followed by supervised fine-tuning

最后呢，推荐给大家基本ML的书籍：

• Chris Bishop, “Pattern Recognition and Machine Learning”, 2007
• Simon Haykin, “Neural Networks: a Comprehensive Foundation”, 2009 (3rd edition)
• Richard O. Duda, Peter E. Hart and David G. Stork, “Pattern Classification”, 2001 (2nd edition)

原文链接：http://blog.csdn.net/abcjennifer/article/details/7826917