cuda neural network

?? The error inverse propagation algorithm is by far the most successful neural network learning algorithm, the use of neural networks in practical tasks, mostly using BP algorithm to train.?? Given training set\ (d={(x_1,y_1), (x_2,y_2),...... (x_m,y_m)},x_i \in r^d,y_i \in r^l\), that is, the input example is\ (d\)Attribute description, Output\ (l\)a result. ,

The previous article mentions the difference between data mining, machine learning, and deep learning: http://www.cnblogs.com/charlesblc/p/6159355.htmlDeep learning specific content can be seen here:Refer to this article: Https://zhuanlan.zhihu.com/p/20582907?refer=wangchuan "Wang Chuan: How deep is the depth of learning, how much did you learn?"(i) "Note: Neural network research, because the artificial int

CNN (convolutional neural Network)Convolutional Neural Networks (CNN) dating back to the the 1960s, Hubel and others through the study of the cat's visual cortex cells show that the brain's access to information from the outside world is stimulated by a multi-layered receptive Field. On the basis of feeling wild, 1980 Fukushima proposed a theoretical model Neocog

It can be considered that artificial neural network is a meta function, it can receive a fixed number of digital input and generate a fixed number of digital output. In most cases, the neural network has a layer of hidden neurons in which the hidden neurons and the input neurons and the output neurons are fully connect

Paste the Experiment Code firstThe target uses the Amore method of the neural network to train the data and then test the data Library (amore)X1 X2 X11 X12 x21 x22 Y1 Y2 P Q Target =y1 NET , Error.criterium = ' LMS ', Stao = Na,hidden.layer = "Tansig",Output.layer = ' Purelin ', method = "ADAPTGDWM")Result , n.shows = 5) zPlot (q[1:100,1],z, col= "Blue", pch= "+")Points (q[1:100,1],y2,col= "Red", pch= "X")

Principles of training multi-layer neural network using backpropagation The project describes teaching process of multi-layer neural network employing backpropagation algorithm. To illustrate this process, the three layer neural

ICML 2016 's article [Noisy Activation Functions] gives the definition of an activation function: The activation function is a map h:r→r and is almost everywhere.The main function of the activation function in neural network is to provide the nonlinear modeling ability of the network, if not specifically, the activation function is generally nonlinear function. A

At present, there are neural networks in all aspects of engineering application, and younger brother is now learning neural network, a little conjecture.Most of the current neural network is to adjust their own weights, so as to learn. Under the structure of a certain

New neural network architectures are in place anytime, anywhere, dcign,iilstm,dcgan~1. Forward propagation Network (FF or FFNN)Very straightforward, they transfer information from the trip (input and output, respectively). Neural networks usually have many layers, including input layers, hidden layers, and output layer

Word vectors:Whether it is a passage or an article, the word is the most basic constituent unit.How to make computers use these words?The point is how to convert a word into a vectorIf in a two-dimensional space, had,has,have meaning is the same, so to be closer.Need,help is very close to the same location.To show the same, related.Let's say the following example:Which words are closer to the Frog frog? SynonymsFor two different languages, the language space is also very close after modeling,So

In front of us, we talked about the DNN, and the special case of DNN. CNN's model and forward backward propagation algorithms are forward feedback, and the output of the model has no correlation with the model itself. Today we discuss another type of neural network with feedback between output and model: Cyclic neural network