convolutional neural network theory

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

1.why Look in case study This week we'll talk about some typical CNN models, and by learning these we can deepen our understanding of CNN and possibly apply them in practical applications or get inspiration from them. 2.Classic Networks The LENET-5 model was presented by Professor Yann LeCun in 1998 and is the first convolutional neural network to be successfull

1 Figure Neural Network (original version)Figure Neural Network now the power and the use of the more slowly I have seen from the most original and now slowly the latest paper constantly write my views and insights I was born in mathematics, so I prefer the mathematical deduction of the first article on the introductio

Transfer from http://www.cnblogs.com/heaad/archive/2011/03/07/1976443.htmlThe main contents of this paper include: (1) Introduce the basic principle of neural network, (2) Aforge.net the method of realizing Feedforward neural Network, (3) Matlab to realize the method of Feedforward

Content Summary:(1) introduce the basic principle of neural network(2) Aforge.net method of realizing Feedforward neural network(3) the method of Matlab to realize feedforward neural network---cited Examples In this paper, fisher'

(Von Neumann, 1945): Elementary operations (such as arithmetic operations), logical control flows (branching loops), and read-write memory. Although there has been widespread success in modeling complex data, modern machine learning theory has generally neglected the use of control flows and memory. Recursive neural networks stand out because of their ability to learn and complex transformations of data wi

bp neural network in BP for back propagation shorthand, the earliest it was by Rumelhart, McCelland and other scientists in 1986, Rumelhart and in nature published a very famous article "Learning R Epresentations by back-propagating errors ". With the migration of the Times, the theory of BP neural

example, you is going to generate an image of the Louvre Museum in Paris (content image C), mixed with a painting By Claude Monet, a leader of the Impressionist movement (style image S). Let's see how you can do this. 2-transfer Learning Neural Style Transfer (NST) uses a previously trained convolutional network, and builds on top of. The idea of using a

, convolutional network (CNN) is to solve this problem and propose a framework.So how do you make the neural network have the transformation invariance I want? We know that the rise of neural networks, to a large extent, is the application of bionics in the field of artifici

Deep Learning paper notes (IV.) The derivation and implementation of CNN convolution neural network[Email protected]Http://blog.csdn.net/zouxy09 I usually read some papers, but the old feeling after reading will slowly fade, a day to pick up when it seems to have not seen the same. So want to get used to some of the feeling useful papers in the knowledge points summarized, on the one hand in the process of

reversal of the convolutional neural network. For example, enter the word "cat" to train the network by comparing the images generated by the network with the real images of the cat, so that the network can produce images more li

realization of Image search algorithm based on convolutional neural network If you use this name to search for papers, there must be a lot. Why, because from a theoretical point of view, convolutional neural networks are ideal for finding similar places in images. Think abou

] [, 3] [, 4][1,] 1 1 0 1Solve XOR logic (same fetch 0, different fetch 1) with result: (Code XOR (C (1,0,1,1), C (1,1,0,1)))[1] False True True falsenamely 0,1,1,0, the classification is correct.Finally, the delta rules can only train a single-layer network, but this does not have a significant impact on its functionality. In theory, multilayer neural networks a

RateConsole.WriteLine ();Console.WriteLine ("Learning rate ===> {0}", teacher. Learningrate);//Number of iterationsintiterations =0;Console.WriteLine (); while(Error >0.001){Error = Teacher. Runepoch (input, output);Console.WriteLine ("Learning error ===> {0}", error);iterations++;}Console.WriteLine ("iterations ===> {0}", iterations);Console.WriteLine ();Console.WriteLine ("SIM:");//Simulation for(inti =0; I 4; i++){Console.WriteLine ("input{0}: ===> {1},{2} sim{0}: ===> {3}", I, input[i][0],

follows:Development historydnn-Definitions and conceptsIn convolutional neural networks, convolution operations and pooling operations are stacked organically together, forming the backbone of the CNN.It is also inspired by the multi-layered network between the macaque retina and the visual cortex, and the deep Neural

1 Introduction Remember when I first contacted RoboCup 2 years ago, I heard from my seniors that Ann (artificial neural network), this thing can be magical, he can learn to do some problems well enough to deal with. Just like us, we can learn new knowledge by studying. But for 2 years, I've always wanted to learn about Ann, but I haven't been successful. The main reason for this is that the introduction o

(EMNLP 2014), 1746–1751.[2] Kalchbrenner, N., Grefenstette, E., Blunsom, P. (2014). A convolutional Neural Network for modelling sentences. ACL, 655–665.[3] Santos, C. N. DOS, Gatti, M. (2014). Deep convolutional neural Networks for sentiment analysis of the short texts.