neural networks and learning machines 3rd edition

Instructor Ge yiming's "self-built neural network writing" e-book was launched in Baidu reading. Home page:Http://t.cn/RPjZvzs. Self-built neural networks are intended for smart device enthusiasts, computer science enthusiasts, geeks, programmers, AI enthusiasts, and IOT practitioners, it is the first and only Neural

want to go down the hill, how can you go down the hill as soon as possible (by default, the speed is constant and you will not die )? You should look around and find the steep current direction to go down the hill? In this direction, the gradient can be used for calculation, which is the source of the gradient descent method. Do you think it is very simple, think you have mastered it? Haha, it's still too young. I will not go into details about this part. I will provide two materials for my stu

This paper summarizes some contents from the 1th chapter of Neural Networks and deep learning. Catalogue Perceptual device S-type neurons The architecture of the neural network Using neural networks to recogni

Learning Goals Understand the convolution operation Understand the pooling operation Remember the vocabulary used in convolutional neural network (padding, stride, filter, ...) Build a convolutional neural network for Image Multi-Class classification "Chinese Translation"Learning GoalsUndersta

Feedforward network, for example, we look at the typical two-layer network of Figure 5.1, and examine a hidden-layer element, if we take the symbol of its input parameter all inverse, take the tanh function as an example, we will get the opposite excitation function value, namely Tanh (−a) =−tanh (a). And then the unit all the output connection weights are reversed, we can get the same output, that is to say, there are two different sets of weights can be obtained the same output value. If ther

Motive (motivation)For non-linear classification problems, if multiple linear regression is used to classify, it is necessary to construct many high-order items, which leads to too many learning parameters, so the complexity is too high.Neural networks (Neural network)As shown in a simple neural network, each circle re

(Net,s_train ', T);Y=sim (Net,s_sample ');Cla=y (:, 1);Case 9% single layer competitive neural networkMm=s_train (:);Mm=minmax (mm ');Q=repmat (Mm,size (s_train,2), 1);NET=NEWC (q,2,0.1);Net=init (NET);net.trainparam.showwindow=0;net.trainparam.epochs=20;Net=train (Net,s_train ');A=sim (Net,s_train ');Cla=vec2ind (s_sample ') ';Case 10%BP Neural NetworkMm=s_train (:);Mm=minmax (mm ');NET=NEWFF (Repmat (Mm,

The use of neural networks to achieve autonomous driving, which means that the car through learning to drive themselves.It is a legend explaining how to realize automatic driving through neural network learning:The lower left corner is an image of the road ahead that the car sees. Left, you can see a horizontal menu ba

Source: Michael Nielsen's "Neural Network and Deep leraning"This section translator: Hit Scir master Xu Zixiang (Https://github.com/endyul)Disclaimer: We will not periodically serialize the Chinese translation of the book, if you need to reprint please contact [email protected], without authorization shall not be reproduced."This article is reproduced from" hit SCIR "public number, reprint has obtained consent. " Using

-cognitive machines, the visual fuzzy quantity brought by C-element in the photosensitive region of each S-element is normally distributed. If the edge of the photosensitive region produces a blurring effect larger than the center, the s-element will accept greater deformation tolerance resulting from this non-normal blur. What we want to get is that the difference between the training pattern and the effect of the deformation stimulation pattern on t

, get S2: Feature map width, high to the original 1/2, that is, 28/2=14, feature map size into 14x14, the number of feature maps is unchanged.Then the second convolution, using 16 convolution cores, obtained the feature map of C3:16 Zhang 10x10.Then the next sampling, get S4: The feature map width, high to the original 1/2, that is, the 10/2=5, the feature map size into 5x5, the number of feature map is unchanged.After entering the convolution layer c5,120 Zhang 1x1 full connection feature map,

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