在学习《深度学习》时,我主要是通过Andrew Ng教授在http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial上提供的UFLDL(Unsupervised Feature Learning and Deep Learning)教程,本文在写的过程中,多有借鉴这个网站提供的资料。 稀疏自编码器(Sparse Autoencoder)可以自动从无标注数据中学习特征,可以给出比原...
Deep Learning1:Sparse Autoencoder 学习stanford的课程http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial一个月以来,对算法一知半解,Exercise也基本上是复制别人代码,现在想总结一下相关内容 1.Autoencoders and Sparsity 稀释编码:Sparsity parameter 隐藏层的平均激活参数为 约束为 为实现这个目标,在cost Fu...
Deep architectureSemi-supervised learningWhite-box modelPart-based representationSummary: In this paper, we demonstrate how complex deep learning structures can be understood by humans, if likened to isolated but understandable concepts that use the architecture of Nonnegativity Constrained Autoencoder (NCAE...
Deep learning:三十七(Deep learning中的优化方法) 。所以不能单从网络的结构来判断其属于Deeplearning中的哪种方法,比如说我单独给定64-100的2层网络,你就无法知道它属于deeplearning中的哪一种方法,因为这个网络既可以...作者paper里面用的是convolution,阅读完code后发现其实现就是一个普通二层的autoencoder。看来...
autoencoder和deep learning的背景介绍:http://tieba.baidu.com/p/2166279134 Pallashadow 9S 12 sparse autoencoder是一种自动提取样本(如图像)特征的方法。把输入层激活度(如图像)用隐层激活度表征,再把隐层信息在输出层还原。这样隐层上的信息就是输入层的一个压缩过的表征,且其信息熵会减小。并且这些表征很...
This post contains my notes on the Autoencoder section of Stanford’s deep learning tutorial / CS294A. It also contains my notes on the sparse autoencoder exercise, which was easily the most challenging piece of Matlab code I’ve ever written!!!
原文链接:http://www.cnblogs.com/JayZen/p/4119061.html 稀疏自编码器的学习结构: 稀疏自编码器Ⅰ: 神经网络 反向传导算法 梯度检验与高级优化 稀疏自编码器Ⅱ: 自编码算法与稀疏性 可视化自编码器训练结果 Exercise: Sparse Autoencoder 稀疏自编码器Ⅰ这部分先简单讲述神经网络的部分,它和稀疏自编码器关系很大...
2.3 收缩式自动编码器(Contractive Autoencoders)在去噪自动编码器(denoising autoencoders)中，重点在于...
In Section 2, we present a detailed introduction on the sparse autoencoder, the deep sparse autoencoders, as well as the applications to the facial expression recognition. Section 3 mainly discusses the experiment results of facial expression recognition via the deep sparse autoencoders and also ...
After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and ...