how to train convolutional neural network

ImageNet classification with deep convolutional neural Networks reading notes(2013-07-06 22:16:36) reprint Tags: deep_learning imagenet Hinton Category: machine learning (after deciding to read a paper each time, the notes are recorded on the blog.) )This article, published in NIPS2012, is Hinton and his students are using deep learning in response to doubts about deep learn

in parentheses, there are many possibilities to think about. But if you give "China ()", the scope of the possibility is reduced. When it comes to "I am China", the likelihood of guessing will increase further. So the natural way is to enlarge the number of vectors, but it will also make the dimensions of the input vector and the weight matrix of the first layer of the neural network increase rapidly. If t

large number of outstanding academics joining the deep neural network, especially the Bengio research group at the University of Montreal and the NG Research Group at Stanford University. From the analysis of the proposed model, an important contribution of the Bengio research group is to propose a deep learning network based on the self-encoder (auto-encoder).

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

is to "share the rights" (weight sharing), which allows a group of neurons to use the same connection right, a strategy that plays an important role in convolutional neural networks (convolutional neural Networks, referred to as CNN). For a CNN network:CNN can train with BP

training set, and the network still has a great chance of recognizing it. It is this generalization that makes the neural network a priceless tool that can be used in countless applications, from face recognition, medical diagnostics, to racing predictions, there is also the navigation of bots in computer games (robots that act as game characters) or hardware ro

Original page: Visualizing parts of convolutional neural Networks using Keras and CatsTranslation: convolutional neural network Combat (Visualization section)--using Keras to identify cats It is well known, that convolutional

"Matlab Neural network Programming" Chemical Industry Press book notesThe fourth Chapter 4.3 BP propagation Network of forward type neural network This article is "MATLAB Neural network

neurons are active, only a very small fraction will be active, the different layers of neurons can not be fully connected. In the back of 5.5.6, we will see an example of the sparse network structure used by convolutional neural networks.We can naturally design a more complex network structure, but in general we have

conclude that the problem it is best at solving is related to the time series. RNN is also the most natural neural network structure for dealing with such problems. The principal structure of a RNN is duplicated several times by the time series, and structure A is also called the loop body. How to design the network structure of loop body A is the key to solve

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