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I have read the following books for half a year in order to graduate. It is recommended that you buy a non-MATLAB version for the "digital image processing" of Gonzalez, which includes the MATLAB version and non-MATLAB version. As for how to use matltab, you can view its help and Demos. Each toolbox has a lot of demos. Very good. In addition, examples of the book visual c ++ _ MATLAB Image Processing and recognition practical case selection are also
. We use the cublas. lib and curand. Lib libraries. One is matrix calculation and the other is random number generation. I applied for all the memory I needed at one time. After the program started running, there was no data exchange between the CPU and GPU. This proved to be very effective. The program performance is about dozens of times faster than the original C language version (if the network is relatively large, it can reach a speed-up ratio of
Artificial neural Network (ANN), or neural network, is a mathematical model or a computational model for simulating the structure and function of biological neural networks. Neural networks are computed by a large number of artifi
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 in the Minit data set effect is good, but there are some bugs, because the recent busy, the
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
above. A feed to the input gate, displayed at the bottom right of the cell. Another feed to the output door, on the top right side of the display. Each blue node is associated with an activation function, typically an S-type function, and a PI node that represents multiplication. The most central node in the cell is called the internal state, and the 1 weight spans the time step, which feeds back itself. The self-connecting edge of the internal state, known as a constant error belt or CEC. In t
adaptation process to produce the correct results, and through constant training to make it a learning function, of course, the algorithm only reflects a number of basic characteristics of the human brain, but not a lifelike description of the biological system, but a simple imitation, simplification and abstraction.The algorithm is different from the digital computer, will follow the procedure to perform the operation step by step, but can adapt themselves to the environment, summarize the rul
, processing combinatorial optimization problems, pattern recognition, image processing, sensor signal processing, robot control, signal processing, health care, medical, economic, chemical industry, Fields of welding, geography, data mining, power systems, transportation, military, mining, agriculture and meteorology.Basic structure of neural networkArtificial neural
a correct result-oriented. At this time we can use its own adaptation process to produce the correct results, and through constant training to make it a learning function, of course, the algorithm only reflects a number of basic characteristics of the human brain, but not a lifelike description of the biological system, but a simple imitation, simplification and abstraction.The algorithm is different from the digital computer, will follow the procedure to perform the operation step by step, but
be thought of as a feature of the network learning. Based on this feature, further similarity comparisons can be made.3rd, the Deep learning algorithm can be effective key is actually large-scale data, the reason is that each DL has a number of parameters, a small amount of data can not be fully trained parameters.The next few words, go straight to the topic to start the CNN tour.Introduction to Convolutional neu
this:According to our experience, if the alphabet can be moved to the center of the field of view, the difficulty of recognition will be reduced a lot, in favor of improving the recognition rate.In this case, if we can change the image to the standard size, we can increase the corresponding recognition rate.For objects of real knowledge, from different angles, t
the global information about the position is lost (in the approximate position of the sentence), the local information captured by the filter is preserved, such as "Not Amazing" and "amazing not" mean very different things.In the field of image recognition, pooling can also provide translational and rotational invariance. If a zone is pooled, even if the image pans/rotates several pixels, the resulting output value is basically the same, because the
This article first Huchi: HTTPS://JIZHI.IM/BLOG/POST/INTUITIVE_EXPLANATION_CNN What is convolutional neural network. And why it's important. convolutional Neural Networks (convolutional neural Networks, convnets or CNNs) are a neural
Microsoft Research Asia chief researcher Sun JianHow accurate is the world's best computer vision system? On December 10 9 o'clock in the morning EST, the imagenet Computer Vision Recognition Challenge was announced--Microsoft Research Asia Vichier's researchers, with the latest breakthroughs in deep neural network technology, have won the title of all three majo
); output = Zeros (s, 3 ); For i = 1:s output (I, Class (i) ) = 1; end% Create a neural network Net = NEWFF (Minmax (Input), [3], {' Logsig ' Purelin '}, ' Traingdx '); % Set Training Parameters net.trainparam.show = Net.trainparam.epochs = Net.trainparam.goal = 0.01; net.trainParam.lr = 0.01;% start Training n ET = train (NET, input, output ');% read test data [T1 T2 t3 t4 c] = textread (' testData.t
This digest from: "Pattern recognition and intelligent computing--matlab technology implementation of the third edition" and "Matlab Neural network 43 Case Analysis" "Note" The Blue font for your own understanding part The advantages of radial basis function neural network:
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 like the cat. DN can be combined with ffnn like
0 Preface Neural network in my impression has been relatively mysterious, just recently learned the neural network, especially the BP neural network has a more in-depth understanding, therefore, summed up the following experience
The construction of Neural Networks (neural network) is inspired by the operation of biological neural network function. Artificial neural networks are usually optimized by a learning method based on mathematical statistics, so ar
HT h_t is the output. To make it easier to describe recurrent neural networks, we expand the diagram above to get:Such a chain-like neural network represents a recurrent neural network, which can be considered as multiple replication of the same
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