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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
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
This article first Huchi: HTTPS://JIZHI.IM/BLOG/POST/INTUITIVE_EXPLANATION_CNN What is convolutional neural network. And why it's important. convolutional Neural Networks (convolutional neural Networks, convnets or CNNs) are a neural network that has proved particularly effective in the field of image recognition an
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
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
Absrtact: As the core technology of most computer vision system, CNN has made great contribution in the field of image classification. Starting from the use case of computer vision, this paper introduces CNN and its advantages in natural language processing and its function.When we hear convolutional neural networks (convolutional neural Network, CNNs), we tend to associate computer vision. CNNs has made gr
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
Deep learning veteran Yann LeCun detailed convolutional neural network The author of this article: Li Zun 2016-08-23 18:39 This article co-compiles: Blake, Ms Fenny Gao Lei Feng Net (public number: Lei Feng net) Note: convolutional Neural Networks (convolutional neural network) is a feedforward neur
A neural probabilistic language model. This paper was published by begio and others in 2003. It can be said that it is the originator of the word expression. A brief translation is provided here. A neural probabilistic Language Model A neural probability Language Model Abstract One goal of the statistical language model is to learn the joint probability functio
Transferred from: http://dataunion.org/11692.htmlZhang YushiSince July this year, has been in the laboratory responsible for convolutional neural networks (convolutional neural network,cnn), during the configuration and use of Theano and Cuda-convnet, Cuda-convnet2. In order to enhance the understanding and use of CNN, this blog post, in order to communicate with people, mutual gain. Before the text, say a
Overview This demo is very suitable for beginners AI and deep learning students, from the most basic knowledge, as long as there is a little bit of advanced mathematics, statistics, matrix of relevant knowledge, I believe you can see clearly. The program is written without the use of any third-party deep Learning Library, starting at the bottom. First, this paper introduces what is neural network, the characteristics of
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
This digest from: "Pattern recognition and intelligent computing--matlab technology implementation of the third edition" and "Matlab Neural network 43 Case Analysis" "Note" The Blue font for your own understanding part The advantages of radial basis function neural network: Approximation ability, classification ability and learning speed are superior to BP neural
These two days in the study of artificial neural networks, using the traditional neural network structure made a small project to identify handwritten numbers as practiced hand. A bit of harvest and thinking, want to share with you, welcome advice, common progress.The usual BP neural network refers to the traditional artificial
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