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

The first day of CNN Basics From:convolutional Neural Networks (LeNet)
neuro-Cognitive machines .The source of CNN's inspiration has been very comprehensive in many papers, and it is the great creature that found receptive Field (the sensation of wild cells). Based on this concept, a neuro-cognitive machine is proposed. Its main function is to recept part of the image information (or characteristics), and then through the hierarchical submission o

value of the C3 layer according to the local gradient δ value. (2) The weight of the C3 layer updates the value. C3 layer 6*12 A 5*5 template, we first define N=1~6,M=1~12 represents the label of the template, S,t represents the location of the parameters in the template (3) Weight update formula of C1 layer and field gradient δ value Similarly, we can also get the C1 layer weight update formula, here the m=6,n=1, and y refers to the input image the sampling layer S2 of the convolution

The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems. Therefore, a more reasonable neural network structure is needed to effectively reduce the number of parameters in the neural net

The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems. Therefore, a more reasonable neural network structure is needed to effectively reduce the number of parameters in the neural net

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 t

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

convolutional Neural Networks (convolution neural network, CNN) have achieved great success in the field of digital image processing, which has sparked a frenzy of deep learning in the field of natural language processing (Natural Language processing, NLP). Since 2015, papers on deep learning in the field of NLP have emerged. Although there must be a lot of arty hydrology, there are many classic application

produce heavyweight results. We will introduce and implement these networks in a follow-up, in addition to the reconstruction of the Theano implementation code, but also to gradually supplement these algorithms in the actual application of the examples, we will mainly apply these algorithms in the start-up company data, from tens of thousands of start-up companies and investment and financing data, It is hoped to find out which companies are more likely to be invested, and which firms are more

,In the above formula, the * number is the convolution operation, the kernel function k is rotated 180 degrees and then the error term is related to the operation, and then summed.Finally, we study how to calculate the partial derivative of the kernel function connected with the convolution layer after obtaining the error terms of each layer, and the formula is as follows.The partial derivative of the kernel function can be obtained when the error item of the convolution layer is rotated 180 deg

Introduction to convolutional Neural Networks
Convolutional neural network is a multi-layer neural network that specializes in processing machine learning problems related to images, especially big images.
The most typical convolutional network consists of a convolution layer, a pooling layer, and a full connection layer. The convolution layer works with the pool

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

Deep Learning paper notes (IV.) The derivation and implementation of CNN convolution neural network[Email protected]Http://blog.csdn.net/zouxy09 I usually read some papers, but the old feeling after reading will slowly fade, a day to pick up when it seems to have not seen the same. So want to get used to some of the feeling useful papers in the knowledge points summarized, on the one hand in the process of

http://blog.csdn.net/diamonjoy_zone/article/details/70576775Reference:1. inception[V1]: going deeper with convolutions2. inception[V2]: Batch normalization:accelerating deep Network Training by reducing Internal covariate Shift3. inception[V3]: Rethinking the Inception Architecture for computer Vision4. inception[V4]: inception-v4, Inception-resnet and the Impact of residual Connections on learning1. PrefaceThe NIN presented in the previous article ma

This paper combines the application of deep learning, convolution neural Network for some basic applications, referring to LeCun's document 0.1 for partial expansion, and results display (in Python).Divided into the following parts:1. Convolution (convolution)2. Pooling (down sampling process)3. CNN Structure4. Run the experimentThe following are described separately.PS: This blog for the ESE machine learni

TensorFlow deep learning convolutional neural network CNN, tensorflowcnn
I. Convolutional Neural Network Overview
ConvolutionalNeural Network (CNN) was originally designed to solve image recognition and other problems. CNN's current applications are not limited to images and

OverviewAlthough the CNN deep convolution network in the field of image recognition has achieved significant results, but so far people to why CNN can achieve such a good effect is unable to explain, and can not put forward an effective network promotion strategy. Using the method of Deconvolution visualization in this

After figuring out the fundamentals of convolutional Neural Networks (CNN), in this post we will discuss the algorithm implementation techniques based on Theano. We will also use mnist handwritten numeral recognition as an example to create a convolutional neural network (CNN) to train the network so that the recogniti

weight reproduction) and time or spatial sub-sampling to obtain some degree of displacement, scale and deformation invariance.3. CNN TrainingThe training algorithm is similar to the traditional BP algorithm. It consists of 4 steps, and these 4 steps are divided into two stages:The first stage, the forward propagation phase:A) Take a sample (X,YP) from the sample set and input X into the network;b) Calculat

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