convolutional neural network wikipedia

neural network, it is necessary to control the number of trainable parameters of two networks, otherwise there is no comparability. In his machine learning video, Professor Li Hongyi, for example, deep performance is better with the same number of parameters, which means that deep parameters will be less if the same effect is achieved. It is not denied that theoretically only a

(Original address: Wikipedia)Introduction:Pulse Neural Network spiking Neuralnetworks (Snns) is the third generation neural network model, the simulation neuron is closer to reality, besides, the influence of time information is considered. The idea is that neurons in a dyna

neural network, it is necessary to control the number of trainable parameters of two networks, otherwise there is no comparability. In his machine learning video, Professor Li Hongyi, for example, deep performance is better with the same number of parameters, which means that deep parameters will be less if the same effect is achieved. It is not denied that theoretically only a

, including neural network structure, forward propagation, reverse propagation, gradient descent and so on. The second part explains the basic structure of convolutional neural network, including convolution, pooling and full connection. In particular, it focuses on the deta

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).

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 algorithm, but in training, whether it is th

;Teacher. Momentum = 0;int iteration = 1;Iterative Training 500 TimesWhile (Iteration {Teacher. Runepoch (Traininput, trainoutput);++iteration;}Using the trained neural network to classify, T is the input data vectorNetwork.compute (t) [0]The iris data is classified by the program, and the recognition rate can reach about 97%.Click to download source codeArticle from: http://www.cnblogs.com/heaad/Reprint pl

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

the Iris data, and the recognition rate can reach about 97%.Click to download source codeReference from: http://www.cnblogs.com/heaad/archive/2011/03/07/1976443.htmlReference Documents:[1] Andrew Kirillov. Neural Networks on C #. [Online].Http://www.codeproject.com/KB/recipes/aforge_neuro.aspx 2006.10[2] Sacha Barber. Ai:neural Network for Beginners. [Online].Http://www.codeproject.com/KB/recipes/NeuralNet

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

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

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