how to train convolutional neural network

introduction to artificial neural networks and OCR software application examples, printed documents are scanned and converted to electronic formats, such as ASCII text, which is easy to operate and/or more effectively stored. Neural Network Software recommendation NeuroSolutionsIs the most popularNeural Network Simul

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

Reprint please indicate the Source: Bin column, Http://blog.csdn.net/xbinworldThis is the essence of the whole fifth chapter, will focus on the training method of neural networks-reverse propagation algorithm (BACKPROPAGATION,BP), the algorithm proposed to now nearly 30 years time has not changed, is extremely classic. It is also one of the cornerstones of deep learning. Still the same, the following basic reading notes (sentence translation + their o

This is the essence of the whole fifth chapter, will focus on the training method of neural networks-reverse propagation algorithm (BACKPROPAGATION,BP), the algorithm proposed to now nearly 30 years time has not changed, is extremely classic. It is also one of the cornerstones of deep learning. Still the same, the following basic reading notes (sentence translation + their own understanding), the contents of the book to comb over, and why the purpose,

Reference papers: 1,convolutional deep Belief NetworksFor Scalable unsupervised learning of hierarchical representations 2.Stacks of convolutional Restricted Boltzmann machinesFor shift-invariant Feature LearningPre-Knowledge:http://blog.csdn.net/zouxy09/article/details/9993371 At the beginning of the article, the author presents the problem of the current multilayer generation model (such as DBN)

minima mainly include increasing impulse terms, using random gradient descent, and using different initial weights to train the network.2) Excessive weight valueWhen the number of hidden nodes, the more layers, weights multiplied. The increase of weight means that the higher the dimension of the corresponding space is, the excessively fitting of the late training is easily caused by the high dimensionality

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 impr

kernel and step operation, There may be the wrong dimension (analogy 2x3 matrix can not be multiplied by the 2x4 matrix, you need to replace the 2x4 matrix into a 3x4 matrix, here is the matrix of the 2x4 to add a row of 0 elements, so that it becomes the matrix of 3x4), the default is 0, preferably set to (kW-1)/ 2, which is the width of the convolution core 1 and then divided by 2. The padh default is PADW, preferably set to (kH-1)/2, which is the high-1 convolution core and then divided by 2

, convolutional network (CNN) is to solve this problem and propose a framework.So how do you make the neural network have the transformation invariance I want? We know that the rise of neural networks, to a large extent, is the application of bionics in the field of artifici

example, you is going to generate an image of the Louvre Museum in Paris (content image C), mixed with a painting By Claude Monet, a leader of the Impressionist movement (style image S). Let's see how you can do this. 2-transfer Learning Neural Style Transfer (NST) uses a previously trained convolutional network, and builds on top of. The idea of using a