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UFLDL Learning notes and programming Jobs: convolutional neural Network (convolutional neural Networks)UFLDL out a new tutorial, feel better than before, from the basics, the system is clear, but also programming practice.In deep learning high-quality group inside listen to some predecessors said, do not delve into other machine learning algorithms, you can direc
Reprint please indicate the Source: Bin column, Http://blog.csdn.net/xbinworldThis is the essence of the whole fifth chapter, will focus on the training method of neural networks-reverse propagation algorithm (BACKPROPAGATION,BP), the algorithm proposed to now nearly 30 years time has not changed, is extremely classic. It is also one of the cornerstones of deep learning. Still the same, the following basic
This is the essence of the whole fifth chapter, will focus on the training method of neural networks-reverse propagation algorithm (BACKPROPAGATION,BP), the algorithm proposed to now nearly 30 years time has not changed, is extremely classic. It is also one of the cornerstones of deep learning. Still the same, the following basic reading notes (sentence translation + their own understanding), the contents o
$ = 1 (The purpose is to omit the bias entry).Our example here is that the value of the latter layer is determined only by the value of the previous layer, which, of course, is not necessarily a definite one. As long as there is no feedback structure, it can be counted as the forward neural network. So here is the derivation except for a structure called the skip layer, where the current layer is not determined by the previous layer, but by the values
Specific principle website: http://wenku.baidu.com/link?url=zSDn1fRKXlfafc_ Tbofxw1mtay0lgth4gwhqs5rl8w2l5i4gf35pmio43cnz3yefrrkgsxgnfmqokggacrylnbgx4czc3vymiryvc4d3df3Self-organizing feature map neural network (self-organizing Feature map. Also called Kohonen Mapping), referred to as the SMO network, is mainly used to solve the problem of pattern recognition class. The SMO network is a unsupervised learning algorithm similar to the previous Kmeans al
relevant people to have a deeper understanding of the business.Another way of thinking about model work is "complex model + simple features". That is, to weaken the importance of feature engineering and to use complex nonlinear models to learn the relationship between features and to enhance their expressive ability. The deep neural network model is such a non-linear model.is a deep neural network with an
ExplainThis allows us to learn to predict a person ' s identity using a Softmax output unit, where the number of classes equals the Number of persons in the database plus 1 (for the final "not in Database" Class).Reasons for the above options error:1, plus 1 explanation error:Put someone's photo into the convolutional neural network, use the Softmax unit to output the kind, or label, to correspond to these different people, or not any of them, so in S
In the deep network, the learning speed of different layers varies greatly. For example: In the back layer of the network learning situation is very good, the front layer often in the training of the stagnation, basically do not study. In the opposite case, the front layer learns well and the back layer stops learning.This is because the gradient descent-based learning algorithm inherently has inherent instability, which causes the learning of the front or back layer to stop.Vanishing gradient p
1 Introduction
An XOR operation is a commonly used calculation in a computer:
0 XOR 0 = 0
0 XOR 1 = 1
1 XOR 0 = 1
1 XOR 1 = 0
We can use the code in the first article to calculate this result Http://files.cnblogs.com/gpcuster/ANN1.rar (need to modify the training set), we can find that the results of learning does not satisfy us, because the single layer of neural network learning ability is limited , you need to use more complex
UFLDL Learning notes and programming Jobs: multi-layer neural Network (Multilayer neural networks + recognition handwriting programming)UFLDL out a new tutorial, feel better than before, from the basics, the system is clear, but also programming practice.In deep learning high-quality group inside listen to some predecessors said, do not delve into other machine l
and FC22 models)
Step3: Full connection layer for reverse propagation and transfer of gradient data back to the convolution layer
STEP4: Convolution layer data with Step2,worker 2 is passed to the fully connected layer for forward propagation
Step5: With Step3, the full-connection layer to achieve reverse propagation, the gradient is returned to the worker 2 corresponding convolution layer
STEP6: Completes the reverse propagation of th
1. Some basic symbols2.COST function================backpropagation algorithm=============1. To calculate something 2. Forward vector graph, but in order to calculate the bias, it is necessary to use the backward transfer algorithm 3. Backward transfer Algorithm 4. Small topic ======== ======backpropagation intuition==============1. Forward calculation is similar to backward calculation 2. Consider only one example, cost function simplification 3. Theta =======implementation Note:unrolling param
{\TEXTBF Y}-\TEXTBF y) ^{\TOP}\TEXT{D}\TEXTBF z) \\=\text{tr} ((\frac{\partial\mathcal L}{\PARTIAL\TEXTBF Z}) ^{\top}\text {D}\TEXTBF Z) \end{aligned}$$This gives the form of the $\delta^{(L)}$:$$\delta^{(l)}=\frac{\partial \mathcal l}{\partial \TEXTBF Z^{(L)}}=\HAT{\TEXTBF Y}-\TEXTBF y$$It is not difficult to see why the $\delta^{(l)}$ is called the error term.Resources:[1] The Lecture Notes on neural networks
+ b.tC. C = a.t + bD. C = a.t + b.t9. Please consider the following code: C results? (If you are unsure, run this lookup in Python at any time). AA = Np.random.randn (3, 3= NP.RANDOM.RANDN (3, 1= a*bA. This will trigger the broadcast mechanism, so B is copied three times, becomes (3,3), * represents the matrix corresponding element multiplied, so the size of C will be (3, 3)B. This will trigger the broadcast mechanism, so B is duplicated three times, becomes (3, 3), * represents matrix multipli
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
The following content is derived from machine learning on Coursera and is based on Rachel-Zhang's blog (http://blog.csdn.net/abcjennifer)
After talking about the two common methods of logisitc regression and linear regression, we need to learn more about other machine learning methods considering some disadvantages,
Abstract:
(1) (2): it helps us understand some basic concepts of neural
.
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 Networks for Digit recognition with PybrainPosted on January. by powel talwar Hi EveryoneAs a part of my B.Tech project, we were required to make a neural network, among other things, which can train on given dat A and perform the task of Digit recognition. We chose Python to do with project in given the wide array of libraries.We aim to identify digits f
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
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 go
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