caffe neural network

gradient descent algorithm to a normalized neural networkThe partial derivative of the normalized loss function is obtained:You can see the paranoid gradient drop. Learning rules do not change:And the weight of learning rules has become:This is the same as normal gradient descent learning rules, which adds a factor to readjust the weight of W. This adjustment is sometimes called weight decay .Then, the normalized learning rule for the weight of the r

Preface body RNN from Scratch RNN using Theano RNN using Keras PostScript "From simplicity to complexity, and then to Jane." "Foreword Skip the nonsense and look directly at the text After a period of study, I have a preliminary understanding of the basic principles of RNN and implementation methods, here are listed in three different RNN implementation methods for reference. RNN principle in the Internet can find a lot, I do not say here, say it will not be better than those, here first recomm

Neural Network Lecture VideoWhat are the neuronts?Storing numbers, returning function values for functionsHow are they connected?a1+ a2+ a3+ A4 +......+ An represents the activation value of the first levelΩ1ω2 ..... Ω7ω8 represents the weight valueCalculates the weighted sum, marks the positive weight value as green, the negative weight value is marked red, the darker the color, the closer the representati

Now that the "neural network" and "Deep neural network" are mentioned, there is no difference between the two, the neural network can not be "deep"? Our usual logistic regression can be thought of as a

Deep neural Network, the problem of pattern recognition, has achieved very good results. But it is a time-consuming process to design a well-performing neural network that requires repeated attempts. This work [1] implements a visual analysis system for deep neural

/***********************************************************************/ /* File: Mc_neuron.h * * 2014-06-04 //////* Description: Single-layer perceptron neural network header file */ /************************************************ / #ifndef _afx_mc_neuron_include_h_ #define _AFX_MC_NEURON_INCLUDE_H_ Class Neuro

Transfer from http://blog.csdn.net/xingzhedai/article/details/53144126More information: http://blog.csdn.net/mafeiyu80/article/details/51446558http://blog.csdn.net/caimouse/article/details/70225998http://kubicode.me/2017/05/15/Deep%20Learning/Understanding-about-RNN/RNN (recurrent Neuron) is a neural network for modeling sequence data. Following the bengio of the probabilistic language model based on

BP algorithm of neural network, gradient test, random initialization of Parameters neural Network (backpropagation algorithm,gradient checking,random initialization)one, cost functionfor a training set, the cost function is defined as:where the red box is circled by a regular term, K: the number of output units is the

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

potentials, are actually some faint currents. So if a neuron wants to deliver a message, it sends a faint current to other neurons through its axis bursts.2 , the yellow circle represents a neuron, X is the input vector, and θ represents the weight of the neuron (which is actually the model parameter we described earlier), and hθ (X) represents the excitation function (in neural network terminology, the ex

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 cons

convolutional neural Network (CNN) is the foundation of deep learning. The traditional fully-connected neural network (fully connected networks) takes numerical values as input.If you want to work with image-related information, you should also extract the features from the image and sample them. CNN combines features,

I. Convolutionconvolutional Neural Networks (convolutional neural Networks) are neural networks that share parameters spatially. Multiply by using a number of layers of convolution, rather than a matrix of layers. In the process of image processing, each picture can be regarded as a "pancake", which includes the height of the picture, width and depth (that is, co

seen before, and if it has a similar word (similar in meaning) to the sentence we have seen, it will have a higher probability, so that it will gain generalization. It is challenging to train such a large model (with millions of parameters) within a reasonable time. The report that we use neural networks to compute probability functions shows that the method presented in two text corpora significantly improves the most advanced n-ary syntax model, an

Just entered the lab and was called to see CNN. Read some of the predecessors of the blog and paper, learned a lot of things, but I think some blog there are some errors, I try to correct here, but also added their own thinking and deduction. After all, the theory of CNN has been put forward, I just want to be able to objectively describe it. If you feel that there is something wrong with this article, be sure to tell me in the comments below.convolutional n