caffe neural network

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

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

How CNN applies to NLP What is convolution and what is convolution neural network is not spoken, Google. Starting with the application of natural language processing (so, how does any of this apply to NLP?).Unlike image pixels, a matrix is used in natural language processing to represent a sentence or a passage as input, and each row of the matrix represents a token, either a word or a character. So each ro

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

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

BP (Back Propagation) network is a multi-layer feed-forward Network trained by the error inverse propagation algorithm, which was proposed by a team of scientists led by Rumelhart and mccelland in 1986, it is one of the most widely used neural networks. The BP network can learn and store a large number of input-output

Series PrefaceReference documents: Rnnlm-recurrent Neural Network Language Modeling Toolkit (click here to read) Recurrent neural network based language model (click here to read) EXTENSIONS of recurrent neural NETWORK LAN

Caffe Gadgets (1)-Visualize network structure \quad recently studied Caffe, but as a former Windows depth user, it is a more accustomed visual interface. However, Caffe is certainly better under the Linux/os x system, because it is usually written in script that is played in the command line. So this is not intuitive,

This article mainly introduces Python based on numpy flexible definition of neural network structure, combined with examples of the principle of neural network structure and python implementation methods, involving Python using numpy extension for mathematical operations of the relevant operation skills, the need for f

Although the research and application of neural network has been very successful, but in the development and design of the network, there is still no perfect theory to guide the application of the main design method is to fully understand the problem to be solved on the basis of a combination of experience and temptation, through a number of improved test, finall

Source: Michael Nielsen's "Neural Network and Deep learning", click the end of "read the original" To view the original English.This section translator: Hit Scir undergraduate Wang YuxuanDisclaimer: If you want to reprint please contact [email protected], without authorization not reproduced. Using neural networks to recognize handwritten numbers

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

This paper aims at constructing probabilistic language model of Chinese based on Fudan Chinese corpus and neural network model.A goal of the statistical language model is to find the joint distribution of different words in the sentence, that is to find the probability of the occurrence of a word sequence, a well-trained statistical language model can be used in speech recognition, Chinese input method, mac

The role of cross-entropy One of the most common ways to solve multi-classification problems with neural networks is to set N output nodes at the last layer, whether in shallow neural networks or in CNN, for example, the last output layer in alexnet has 1000 nodes:And even if the ResNet cancels the all-connected layer, it will have a 1000-node output layer at the end: In general, the number of nodes in the

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