dnn neural network

This chapter does not involve too many neural network principles, but focuses on how to use the Torch7 neural networkFirst require (equivalent to the C language include) NN packet, the packet is a dependency of the neural network, remember to add ";" at the end of the statem

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

TensorFlow deep learning convolutional neural network CNN, tensorflowcnn I. Convolutional Neural Network Overview ConvolutionalNeural Network (CNN) was originally designed to solve image recognition and other problems. CNN's current applications are not limited to images and

First, IntroductionIn machine learning and combinatorial optimization problems, the most common method is gradient descent method. For example, BP Neural network, the more neurons (units) of multilayer perceptron, the larger the corresponding weight matrix, each right can be regarded as one degree of freedom or variable. We know that the higher the freedom, the more variables, the more complex the model, th

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 fiel

In the previous article, we saw how neural networks use gradient descent algorithms to learn their weights and biases. However, we still have some explanations: we did not discuss how to calculate the gradient of the loss function. This article will explain the well-known BP algorithm, which is a fast algorithm for calculating gradients.The inverse propagation algorithm (backpropagation ALGORITHM,BP) was presented at 1970s, but its importance was not

Preface This article first introduces the build model, and then focuses on the generation of the generative Models in the build-up model (generative Adversarial Network) research and development. According to Gan main thesis, gan applied paper and gan related papers, the author sorted out 45 papers in recent two years, focused on combing the links and differences between the main papers, and revealing the research context of the generative antagoni

Tricks efficient BP (inverse propagation algorithm) in neural network trainingTricks efficient BP(inverse propagation algorithm) in neural network training[Email protected]Http://blog.csdn.net/zouxy09tricks! It's a word that's filled with mystery and curiosity. This is especially true for those of us who are trying to

Turn from: Http://matlabbyexamples.blogspot.com/2011/03/starting-with-neural-network-in-matlab.htmlThe Neural Networks is A-to-model any-input to output relations based-some input output data when nothing was known about the model. This example shows your a very simple example and its modelling through neural

The neural network is used to deal with the nonlinear relationship, the relationship between input and output can be determined (there is a nonlinear relationship), can take advantage of the neural network self-learning (need to train the data set with explicit input and output), training after the weight value determi

convolutional Neural Network (convolutional neural network,cnn), weighted sharing (weight sharing) network structure reduces the complexity of the model and reduces the number of weights, which is the hotspot of speech analysis and image recognition. No artificial feature ex

BP (backward propogation) neural networkSimple to understand, neural network is a high-end fitting technology. There are a lot of tutorials, but in fact, I think it is enough to look at Stanford's relevant learning materials, and there are better translations at home: Introduction to Artificial neural