convolutional neural network theory

0. Statement It was a failed job, and I underestimated the role of scale/shift in batch normalization. Details in the fourth quarter, please take a warning. First, the preface There is an explanation for the function of the neural network: It is a universal function approximation. The BP algorithm adjusts the weights, in theory, the

distribution or probability model of the predicted results and samples of the degree of fit. The lower the confusion, the better the degree of fit. The calculation of the confusion histogram is shown in Flow 2:Figure 2 The construction process of the confusion histogram. (a) Sampled-area instances of the sensed region, (b) the excitation of the neurons in each area of the perceptual region, the color mapping of the excitation value, (c) the excitation of a series of neurons in the layer is tran

skip over 1 or 3 or other numbers to see what happens. The question is difficult to answer, but the question itself is not meaningless. First of all, skip 1 or 3, each different link is a new network topology, with different classification capabilities. Because the structure of neural network itself is very complex, it is difficult to discuss two kinds of learni

This article by the @ Star Shen Pavilion Ice language production, reproduced please indicate the author and source. article link: http://blog.csdn.net/xingchenbingbuyu/article/details/53674544 Micro Blog: http://weibo.com/xingchenbing Gossip less and start straight. Since it is to be implemented in C + +, then we naturally think of designing a neural network class to represent the

Deep Learning Neural Network pure C language basic edition, deep Neural Network C Language Today, Deep Learning has become a field of fire, and the performance of Deep Learning Neural Networks (DNN) in the field of computer vision is remarkable. Of course,

Convolution neural Network (convolutional neural Network, CNN) is a feedforward neural network, which is widely used in computer vision and other fields. This article will briefly intro

sentence).Synchronized input and output sequences (such as video classification, we will label each frame of the video).Note that the length of the sequence is not predetermined in each case because the cyclic transformation (green part) is fixed and we want to use it several times. As you would expect, the sequence system is much more powerful than a fixed network that has been set up from the beginning with computational steps. And for people like

OverviewAlthough the CNN deep convolution network in the field of image recognition has achieved significant results, but so far people to why CNN can achieve such a good effect is unable to explain, and can not put forward an effective network promotion strategy. Using the method of Deconvolution visualization in this paper, the author discovers some problems of alexnet, and makes some improvements on the

6th Chapter Image Recognition and convolution neural network 6.1 image recognition problems and the classic data set 6.2 convolution neural network introduction 6.3 convolutional neural networ