Http://blog.sina.com.cn/s/blog_98238f850102w7ik.htmlAll the current Ann neural network algorithm Daquan(2016-01-20 10:34:17)reproduced
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Overview1 BP Neural network1.1 Main functions1.2 Advantages and Limitations2 RBF (radial basis function) neural network2.1 Main functions2.2 Advantages and Limitations3 Sensor
Content Summary:(1) introduce the basic principle of neural network(2) Aforge.net method of realizing Feedforward neural network(3) the method of Matlab to realize feedforward neural network---cited Examples In this paper, fisher's iris data set is used as a test data set of neural network Program. The Iris data set ca
All the current Ann neural network algorithm DaquanOverview1 BP Neural network1.1 Main functions1.2 Advantages and Limitations2 RBF (radial basis function) neural network2.1 Main functions2.2 Advantages and Limitations3 Sensor Neural Network3.1 Main functions3.2 Advantages and Limitations4 Linear
Why use convolution?
In traditional neural networks, such as Multilayer perceptron (MLP), whose input is usually a feature vector, requires manual design features, and then the values of these features to form a feature vector, in the past decades of experience, the characteristics of artificial found is not how to use, sometimes more, sometimes less, Sometimes the selected features do not work at all (the truly functional feature is inside the vast u
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AI technology in game programming.
.(Serialization II)
3Digital neural networks (the digital version)
We have seen that the biological brain is composed of many neural cells. Similarly, the artificial neural network ANN that simulates the brain is composed of many artificial neural cells (Artificial Ne
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. Generally speaking, the past 20 years of artificial neu
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 cannot escape from the above basic knowledge points. So in learning if you can just hold the ab
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 neural network.Section 0 section, introduction example In this paper, Fisher's Iri
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), the sampling layer (pooling layer), the fully connected layer (hidden layers), the outpu
Introduction to machine learning--talking about neural network
This article transferred from: http://tieba.baidu.com/p/3013551686?pid=49703036815see_lz=1#Personal feel is very full, especially suitable for contact with neural network novice.
Start with the question of regression (Regression). I have seen a lot of people say that if you want to achieve strong AI, you have to let the machine learn to observe
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 improved and updated, which has undoubtedly become one of the most widely used
6th Chapter Image Recognition and convolution neural network 6.1 image recognition problems and the classic data set 6.2 convolution neural network introduction 6.3 convolutional neural network common structure 6.3.1 convolution layer 6.3.2 Pool Layer 6.4 Classic convolutional neural network model 6.4.1 LENET-5 model 6
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
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 development of neural networks has gone through 3 stages: 1947-1969 years early, during which tim
The original book: "AI Technology in Game programming"
Excerpt from: http://blog.csdn.net/starxu85/article/details/3143533
Original: http://blog.csdn.net/zzwu/article/category/243067
. (one of the serials) introduce neural networks in normal language(neural Networks in Plain 中文版)
Because we don't have a good understanding of the brain, we often try to use the latest technology as a model to exp
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), the sampling layer (pooling layer), the fully connected layer (hidden layers), the outpu
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 CNN[5] Deep Learning Model: CNN convolution
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 artificial neuron junctions. In most cases, the artificial
Overview
This is the last article in a series on machine learning to predict the average temperature, and as a last article, I will use Google's Open source machine learning Framework TensorFlow to build a neural network regression. About the introduction of TensorFlow, installation, Introduction, please Google, here is not to tell.
This article I mainly explain several points: Understanding artificial Neural
Order:
This series is based on the neuralnetwork and deep learning book, and I have written my own insights. I wrote this series for the first time. What's wrong! Next, we will introduce neural networks so that you can understand what neural networks are. For better learning, we will be guided by identification numbers later. Let's study it step by step!
Let's talk about some of them first! Sometimes do you
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