simple neural network example

image restoration, language processing, pattern recognition and so on. Thirdly, the BP neural network has a strong ability to identify and classify the external input samples. Because of its powerful nonlinear processing ability, the nonlinear classification can be better carried out, which solves the problem of nonlinear classification in the history of neural

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

than the simple gradient descent method, but in the actual application is still not enough speed, these two methods are usually only used for incremental training.Multi-layer neural network can be applied in linear and nonlinear systems, and the approximation of arbitrary function is simulated. Of course, perceptron and linear

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

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

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

calculation, the result is the same.In this example, there are differences in the results, indicating that there must be random components in the system.The random parts of machine learning are usually as follows: 1. The disorderly sequence operation of the training sample; 2. Random gradient descent; 3. The model randomly assigns the initial value.In this example, there is one more: the initial input of t

several layers of neural networks. They are called the input layer, output layer, and several intermediate layers. Each layer of neurons only outputs information from the next layer of neurons, and only receives information from the previous layer of neurons. Is an example of a simple layer-3 neural

Deep Learning Notes (i): Logistic classificationDeep learning Notes (ii): Simple neural network, back propagation algorithm and implementationDeep Learning Notes (iii): activating functions and loss functionsDeep Learning Notes: A Summary of optimization methods (Bgd,sgd,momentum,adagrad,rmsprop,adam)Deep Learning Notes (iv): The concept, structure and code annot

"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

should focus on. It also reduces the parameters of the neural network. parameter Sharing (parameter sharing): The parameters of the filter in the same convolutional layer are shared, and a filter in the filter matrix is the same regardless of the location of the convolution operation. (Of course, the same layer different filter parameters, different layers between the filter parameters are not the same.

neurons are active, only a very small fraction will be active, the different layers of neurons can not be fully connected. In the back of 5.5.6, we will see an example of the sparse network structure used by convolutional neural networks.We can naturally design a more complex network structure, but in general we have

between the filter parameters are not the same.) Sharing the parameters of the filter allows the content in the image to be unaffected by the position. Take mnist handwritten numeral recognition as an example, whether the number "1" appears in the upper left or bottom right corner, the type of picture is unchanged. Sharing the parameters of the convolution filter can also drastically reduce the parameters on the