深度学习是机器学习的一个新分支，主要研究数据的深度层次模型。它的灵感来源于人类大脑明显的深层（分层）结构。对深度学习理论的一个很好的概述是学习人工智能的深度架构。

Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data. It is inspired by the human brain’s apparent deep (layered, hierarchical) architecture. A good overview of the theory of Deep Learning theory is Learning Deep Architectures for AI

For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.

The Next Generation of Neural Networks (Hinton, 2007)

Recent Developments in Deep Learning (Hinton, 2010)

Unsupervised Feature Learning and Deep Learning (Ng, 2011)

If you use this toolbox in your research please cite Prediction as a candidate for learning deep hierarchical models of data

@MASTERSTHESIS{IMM2012-06284,
author = “R. B. Palm”,
title = “Prediction as a candidate for learning deep hierarchical models of data”,
year = “2012”,
}
Contact: rasmusbergpalm at gmail dot com

Directories included in the toolbox
NN/ - A library for Feedforward Backpropagation Neural Networks

CNN/ - A library for Convolutional Neural Networks

DBN/ - A library for Deep Belief Networks

SAE/ - A library for Stacked Auto-Encoders

CAE/ - A library for Convolutional Auto-Encoders

util/ - Utility functions used by the libraries

data/ - Data used by the examples

tests/ - unit tests to verify toolbox is working

For references on each library check REFS.md

下载地址：

https://url92.ctfile.com/f/1850492-576211556-1ca47b?p=3660 （访问密码：3660）