The ① Artificial Neural Network (ANN) is a widely connected giant system. Neuro-scientific research shows that the main part of the human central nerve cortex is composed of 10[11]~10[12] neurons, each neuron has a 10[1]~10[5] synapse, Synapse is a junction between neurons, determining the strength and nature of the connection between neurons. This suggests that the cerebral cortex is an extensively connect
Public Course address:Https://class.coursera.org/ml-003/class/index
INSTRUCTOR:Andrew Ng 1. Cost Function (
Cost functions
)
The last lecture introduced the multiclass classification problem. The difference between the multiclass classification problem and the binary classification problem lies in that there are multiple output units, which are summarized as follows:
At the same time, we also know the price functions of Logistic regression as follows:
The first half repres
What's RNN?
The cyclic neural network, the recurrent neural network, is proposed mainly to deal with sequence data and what sequence data is. is the previous input and the back of the input is related, such as a word, before and after the words are related, "I am hungry, ready to go to XX", according to the input of t
In this paper, a simple handwriting recognition system is realized by BP neural network.First, the basic knowledge1 environmentpython2.7Need to numpy and other librariesCan be installed with sudo apt-get install python-2 Neural Network principleHttp://www.hankcs.com/ml/back-propagation-neural-network.htmlIt is particul
AlexNet:
(ILSVRC Top 5 test error rate of 15.4%)
the first successful display of the convolutional neural network potential network structure.
key point: with a large amount of data and long-time training to get the final model, the results are very significant (get 2012 classification first) using two GPU, divided into two groups for convolution. Since Alex
Original articleReprint please register source HTTP://BLOG.CSDN.NET/TOSTQ the previous section we introduce the forward propagation process of convolutional neural networks, this section focuses on the reverse propagation process, which reflects the learning and training process of neural networks. Error back propagation method is the basis of neural
I saw the time series prediction using dynamic neural networks on the matlat Chinese forum.
Http://www.ilovem http: // A http: // tlab.cn/thread-113431-1.html
(1) first basic knowledge needs to be known
Training data)
Validation Data)
Test Data)
However, I do not quite understand the three. Thank you for your explanation.
The following is an explanation of a Website:
Http://stackoverflow.com/questions/2976452/whats-the-diference-between-train-validat
Introduction
Neural network is the foundation of deep learning, and BP algorithm is the most basic algorithm in neural network training. Therefore, it is an effective method to understand the depth learning by combing the neural network
Neural NETWORKS, part 3:the NETWORKWe have learned on individual neurons in the previous section, now it's time to put them together to form an actual neu RAL Network.The idea was quite simple–we line multiple neurons up to form a layer, and connect the output of the first layer to the I Nput of the next layer. Here are an illustration:Figure 1:neural the network
BP (back propagation) neural network was proposed by the team of scientists led by Rumelhart and McCelland in 1986, which is one of the most widely used neural network models, which is a multilayer Feedforward network trained by error inverse propagation algorithm. The BP
Python implements basic model of a single hidden layer Neural Network
As a friend, I wrote a python code for implementing the Single-hidden layer BP Ann model. If I haven't written a blog for a long time, I will send it by the way. This code is neat and neat. It simply describes the basic principles of Ann and can be referenced by beginners of machine learning.
Several important parameters in the model:
1.
Course Address: https://class.coursera.org/ntumltwo-0021. What are the motivations of neural networks (nnet)?A single perceptron (Perceptron) model is simple, limited in capability and only linearly segmented. It is easy to implement logic and, or, non, and convex sets by combining the perceptual machine model, but it is not possible to achieve the XOR operation and the ability is limited. Multi-level perceptual machine (perceptrons) model, not only c
This paper mainly records the cost function of neural network, the usage of gradient descent in neural network, the reverse propagation, the gradient test, the stochastic initialization and other theories, and attaches the MATLAB code and comments of the relevant parts of the course work.
Concepts of
Vggnet Vggnet is a deep convolutional neural network developed by the computer Vision Group of Oxford University and a researcher at Google DeepMind. Vggnet explores the relationship between the depth of convolutional neural networks and their performance, and vggnet successfully constructs a convolutional neural
Learning/Training Algorithm classification
The different types of neural networks correspond to different kinds of training/learning algorithms. Therefore, according to the classification of neural networks, the traditional neural network learning algorithms can be divided into the following three categories:
1 feedfor
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")
around Microsoft "Xiaoice" the Dog has a "fog" (ie difficult to understand), until this year 2 Month 6 Microsoft technology Executive Sun Jian published an article confirming that the "Xiaoice Dog" feature is Microsoft's proprietary "Artificial Neural network" ( ANN to the vast number of users to provide a network service, so far, this piece of "fog" is gradually
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