%2006年Hilton在《science》上发表文章《Reducing the Dimensionality of Data with Neural Networks》开创了深度学习的先河，从此深度学习大火至今。 %这里给出的就是这篇文章里使用的源代码，采用玻尔兹曼机进行数据降维。是深度学习及玻尔兹曼机入门方面非常好的材料。 %使用方法：训练一个AutoEncoder可选取以下一个程序进行组合： mnistdeepauto.m； 主程序 converter.m 将原始的MNIST数据集转换为
Code provided by Ruslan Salakhutdinov and Geoff Hinton Permission is granted for anyone to copy, use, modify, or distribute this program and accompanying programs and documents for any purpose, provided this copyright notice is retained and prominen
Contents 1. Introduction to Deep Learning (DL) in Neural Networks (NNs)...................................................................................................................................... 86 2. Event-oriented notation for activatio
hinton论文代码注解 Matlab示例代码为两部分，分别对应不同的论文： 1. Reducing the Dimensionality of data with neural networks ministdeepauto.m backprop.m rbmhidlinear.m 2. A fast learing algorithm for deep belief net mnistclassify.m backpropclassfy.m
用最简单的模型、最简单的特征工程做出好效果，追求的就是极致性价比。如果有需要，可以在此基础上做一些模型更改和特征工程，提高表现效果。ture for face verification developed by Chopra, Hadsell, and This forces the LSTm to entirely capture the semantic dif-
LeCun(2005), which utilizes symmetric Conv Nets where ferences d
