convolutional neural network example

Sparse Interactions, receptive Field and Parameter sharing are the core parts of the entire CNN depth network, and we use this article to specifically analyze its principles.First we consider the output matrix of the Feedforward neural network,l layer, which is equal to the input matrix of L layer and the weight matrix of L layer to do matrix multiplication, then

"This paper presents a comprehensive overview of the depth of neural network compression methods, mainly divided into parameter pruning and sharing, low rank decomposition, migration/compression convolution filter and knowledge refining, this paper on the performance of each type of methods, related applications, advantages and shortcomings of the original analysis. ” Large-scale

neural network:step by StepWelcome to your Week 4 assignment (Part 1 of 2)! You are previously trained a 2-layer neural Network (with a single hidden layer). This week, you'll build a deep neural network with the as many layers as you want! In this notebook, you'll

demonstrate the training and use of a task This is the first part: Gitbook Initial Source Address: Recurrent neural network--Introduction There are a lot of dynamic diagrams, please click to watch, can not see the words suggested to go above the Gitbook address reading recurrent neural network--Introduction of timing

Yann LeCun of New York University in 1998 and has been widely used in image classification (including handwriting recognition, traffic sign identification, etc.). For example, in the IJCNN2011-year traffic sign recognition competition, a group of Swiss researchers used a convolutional neural network approach to the ja

Deep Learning Neural Network pure C language basic edition, deep Neural Network C Language Today, Deep Learning has become a field of fire, and the performance of Deep Learning Neural Networks (DNN) in the field of computer vision is remarkable. Of course,

Convolution neural Network (convolutional neural Network, CNN) is a feedforward neural network, which is widely used in computer vision and other fields. This article will briefly intro

solved. But the more neurons there are, the lower the speed of the network, and for that reason, and for several other reasons (which I will explain in chapter 9th), the size of the network is always required to remain as small as possible.I can imagine that you may have been a little dazed about all this information. I think the best thing I can do in this situation is to introduce you to a practical

"Matlab Neural network Programming" Chemical Industry Press book notesThe fourth Chapter 4.3 BP propagation Network of forward type neural network This article is "MATLAB Neural network

other structures to run, is a good article ah. However, after coding a bunch of code, I found a major bug, and in the batch normalization layer, I only considered the impact of scale and forgot another key factor: shift, which actually has a greater impact on the functions that the network expresses. Note that all lines appear to point to the midpoint of the image in the image generated by Sigmoid+batch normalization above. This is because if the s

conclude that the problem it is best at solving is related to the time series. RNN is also the most natural neural network structure for dealing with such problems. The principal structure of a RNN is duplicated several times by the time series, and structure A is also called the loop body. How to design the network structure of loop body A is the key to solve

is to "share the rights" (weight sharing), which allows a group of neurons to use the same connection right, a strategy that plays an important role in convolutional neural networks (convolutional neural Networks, referred to as CNN). For a CNN network:CNN can train with BP algorithm, but in training, whether it is th

Original page: Visualizing parts of convolutional neural Networks using Keras and CatsTranslation: convolutional neural network Combat (Visualization section)--using Keras to identify cats It is well known, that convolutional

extent will find some of the deeper learning rate is lower. The design of the deep residual network is to overcome the problem that the learning rate is low and the accuracy rate cannot be improved effectively because of the depth of the network, also known as the degradation of the network. Even in some scenarios, the increase in the number of layers in the

Building your Deep neural network:step by step Welcome to your Week 4 assignment (Part 1 of 2)! You are have previously trained a 2-layer neural network (with a single hidden layer). This week is a deep neural network with as many layers In this notebook, you'll implement t

can think of St as the Memory unit of the network, St captures the information that occurs all the time before. The output OT is computed only according to the memory of the time t. As is briefly mentioned above, it is slightly more complicated in practice because St usually cannot capture the information of a long time ago. Unlike traditional deep neural networks that use different parameters at each laye

network can be used to study classification problems , of course, the premise is that the problem of classification is linearly divided, which is the same as the limitations of perceptron. "Example 4-13" application Newlin design a dual-input single-output linear neural network, the input vector range is [-1 1;-1 1],

ideas. In short, the neural network contains several major elements: neuronal nodes, layers (layer), weights (weights) and biases (bias). The two computational processes of neural networks are forward propagation and reverse propagation respectively. The forward propagation of each layer contains the weighted sum (convolution) respectively. The linear operation

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 paper, the author discovers some problems of alexnet, and makes some improvements on the

P1038 neural network and p1038 Neural NetworkBackground Artificial Neural Network (Artificial Neural Network) is a new computing system with self-learning ability. It is widely used in