cnn network

TravelseaLinks: https://zhuanlan.zhihu.com/p/22045213Source: KnowCopyright belongs to the author. Commercial reprint please contact the author for authorization, non-commercial reprint please specify the source.In recent years, the Deep convolutional Neural Network (DCNN) has been significantly improved in image classification and recognition. Looking back from 2014 to 2016 of these two years more time, has sprung up R-

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 neural networks (CNNs or Convnets) has been the source of many major breakthroughs in The field of deep learning in the last few years, but they is rather unintuitive to reason on for most people. I ' ve always wanted to break th

redundant and unimportant parameters. Based on the method of low rank decomposition (Low-rank factorization), matrix/tensor decomposition is used to estimate the most informative parameters in deep CNN. Based on the migration/compression convolution filter (Transferred/compact convolutional filters) method, a special structure convolution filter is designed to reduce the complexity of storage and computation. Knowledge refinement (knowledge distillat

Welcome reprint, Reprint Please specify: This article from Bin column Blog.csdn.net/xbinworld.Technical Exchange QQ Group: 433250724, Welcome to the algorithm, technology interested students to join.Recently, the next few posts will go back to the discussion of neural network structure, before I in "deep learning Method (V): convolutional Neural network CNN Class

extent will find some of the deeper learning rate is lower. The design of the deep residual network is to overcome the problem that the learning rate is low and the accuracy rate cannot be improved effectively because of the depth of the network, also known as the degradation of the network. Even in some scenarios, the increase in the number of layers in the

using the API functions provided by the Windows SDK GetAdaptersInfo () can obtain the network card name of all network cards, network card description, network card MAC address, network card IP, network card type and other informa

, so we mainly explain the implementation of Lenet-5 behind. first, the theoretical stage As an introductory article on CNN, there is no intention to nag too much, because what weights share, local feel wild, talk so much, are the related theories of biology, look at those things, most beginners have been bored. convolutional neural Network related blog post is also a lot of, but speaking, basically is copi

adversarial nerworks 5.1 dcgan Ideas DCGAN[1] This paper does not seem to be a great innovation, but in fact, its open source code is now used and the most frequent reference. All this must be attributed to the work of Lapgan [2] More robust of engineering experience to share. That is, Dcgan,deep convolutional generative adversarial Networks, the work [1], points out many of the important architectural designs for this unstable learning approach to GAN and the specific experience of this

model structures in Figure 1, we need to look at one of the deep-learning Troika ———— LeCun's lenet network structure. Why to mention LeCun and lenet, because now visually these artifacts are based on convolutional neural Network (CNN), and LeCun is CNN Huang, Lenet is lecun to create the

Objective From the understanding of convolution nerves to the realization of it, before and after spent one months, and now there are still some places do not understand thoroughly, CNN still has a certain difficulty, not to see which blog and one or two papers on the understanding, mainly by themselves to study, read the recommended list at the end of the reference. The current implementation of the CNN i

certain assumption. What assumption? You'll know later.1. CNN FeaturesCNN stands out from traditional NN in 3 area: Sparse Interaction (Connection) Parameter sharing Equivariant representation. Actually the third feature is more like a result of the first 2 features. Let's go through them one by one. Fully Connected NN nn with Sparse connection Sparse Interaction ,

are connected to hidden layers.CNN's MutationDue to a significant portion of the hidden layer in the RELU,CNN, the value will be 0. Thus, in the measurement of relevance, it is found to be such a child.On the left is the original, the middle is the correlation between layer1 and layer2, note that the correlation with the weight parameter is different things. The more white the dots are approaching 0. Then rearrange the dependencies, as shown in the i

Display and disappear of qq network status bar of a mobile phone; display when there is no network; automatically disappear when there is a network; click the network bar to set the network, qq Follow finddreams, share and make progress together:Http://blog.csdn.net/finddre