neural network stata

A reference to the artificial neural network should think of three basic knowledge points: One is the neuron model, the other is the neural network structure, and the third is the learning algorithm. There are many kinds of neural networks, but the classification basis canno

1 Introduction Remember when I first contacted RoboCup 2 years ago, I heard from my seniors that Ann (artificial neural network), this thing can be magical, he can learn to do some problems well enough to deal with. Just like us, we can learn new knowledge by studying. But for 2 years, I've always wanted to learn about Ann, but I haven't been successful. The main reason for this is that the introduction o

Deep learning--the artificial neural network and the upsurge of researchHu XiaolinThe artificial neural network originates from the last century 40 's, to today already 70 years old. Like a person's life, has experienced the rise and fall, has had the splendor, has had the dim, has had the noisy, has been deserted. Gen

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

Transfer from http://www.cnblogs.com/heaad/archive/2011/03/07/1976443.htmlThe main contents of this paper include: (1) Introduce the basic principle of neural network, (2) Aforge.net the method of realizing Feedforward neural Network, (3) Matlab to realize the method of Feedforward

First, prefaceAfter a period of accumulation, for the neural network, has basically mastered the Perceptron, BP algorithm and its improvement, Adaline and so on the most simple and basic knowledge of feedforward neural network, the following is based on the feedback neural

absrtact : This paper will analyze the basic principle of deep neural network to recognize graphic images in detail. For convolutional neural Networks, this paper will discuss in detail the principle and function of each layer in the network in the image recognition, such as the convolution layer (convolutional layers)

bp neural network in BP for back propagation shorthand, the earliest it was by Rumelhart, McCelland and other scientists in 1986, Rumelhart and in nature published a very famous article "Learning R Epresentations by back-propagating errors ". With the migration of the Times, the theory of BP neural network has been imp

At present, deep learning (Deepin learning, DL) in the field of algorithm is rounds, now is not only the Internet, artificial intelligence, the life of the major areas can reflect the profound learning led to the great change. To learn deep learning, first familiarize yourself with some basic concepts of neural networks (neural Networks, referred to as NN). Of course, the

Introduction of artificial neural network and single-layer network implementation of and Operation--aforge.net Framework use (v)The previous 4 article is about the fuzzy system, it is different from the traditional value logic, the theoretical basis is fuzzy mathematics, so some friends looking a little confused, if interested in suggesting reference related book

http://m.blog.csdn.net/blog/wu010555688/24487301This article has compiled a number of online Daniel's blog, detailed explanation of CNN's basic structure and core ideas, welcome to exchange.[1] Deep Learning Introduction[2] Deep Learning training Process[3] Deep learning Model: the derivation and implementation of CNN convolution neural network[4] Deep learning Model: the reverse derivation and practice of

Neural network concepts and suitability fieldsThe earliest research of neural network was proposed by the 40 psychologist McCulloch and mathematician Pitts, and their MP model was the prelude of Neural Network research.The develop

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

lstm Neural network in simple and lucid Published in 2015-06-05 20:57| 10,188 Times Read | SOURCE http://blog.terminal.com| 2 Reviews | Author Zachary Chase Lipton lstm Recurrent neural network RNN long-term memory Summary:The LSTM network has proven to be more effective t

absrtact : This paper will analyze the basic principle of deep neural network to recognize graphic images in detail. For convolutional neural Networks, this paper will discuss in detail the principle and function of each layer in the network in the image recognition, such as the convolution layer (convolutional layers)

Reprint: http://www.cnblogs.com/zhijianliutang/p/4050931.htmlObjectiveThis article continues our Microsoft Mining Series algorithm Summary, the previous articles have been related to the main algorithm to do a detailed introduction, I for the convenience of display, specially organized a directory outline: Big Data era: Easy to learn Microsoft Data Mining algorithm summary serial, interested children shoes can be viewed, Before starting the Microsoft Neural