caffe neural network

Author: one person 1. Deep neural networks are suitable for any field Depth neural network (deep neural Networks,DNN has made breakthrough advances in image classification, speech recognition, and natural language processing over the past few years. The application in practice has proved that it can be used as a very e

Reprint: http://www.cnblogs.com/zhijianliutang/p/4067795.htmlObjectiveFor some time without our Microsoft Data Mining algorithm series, recently a little busy, in view of the last article of the Neural Network analysis algorithm theory, this article will be a real, of course, before we summed up the other Microsoft a series of algorithms, in order to facilitate everyone to read, I have specially compiled a

Neural network and deep learning the book has been read several times, but each time there will be a different harvest. DL field of paper, every day there will be a lot of new idea out, I think, in-depth reading classic books and paper, must be able to find Remian open problems, so there is a different perspective.Ps:blog is a summary of important contents in the main extract book.Summary section

Weight vector W, training sample X1. Initialize the weight vector to 0, or initialize each component to any decimal between [0,1]2. Input the training sample into the Perceptron to get the classification result (-1 or 1)3. Update weight vectors based on classification resultsPerceptron algorithm for Tuyi data samples that are linearly delimitedMachine learning--perceptron data classification algorithm step (MU-class network-to achieve a simple

Pybrain is a well-known Python neural network library, today I used it to do an experiment, referring to this blog, thanks to the original author, gave a specific implementation, the code can be directly copied to run.Our main problems are as follows:First we give a function to construct the dataset that is required to generate this problem . Def generate_data (): "" " generate original data of U and Y

Abstract: With the development of computational intelligence, artificial neural network has been developed. The industry now considers that it may not be appropriate to classify neural networks (NN) in artificial intelligence (AI), and that the classification of computational Intelligence (CI) can explain the nature of the problem. Some topics in evolutionary com

Neural networks have been very hot, there has been a period of depression, now because of deep learning reasons to continue to fire up. There are many kinds of neural networks: forward transmission network, reverse transmission network, recurrent neural

Neural network and deep learning the book has been read several times, but each time there will be a different harvest.The paper of DL field is changing rapidly. There's a lot of new idea coming out every day, I think. In-depth reading of classic books and paper, you will be able to find Remian open problems. So there's a different perspective.Ps:blog is a summary of important contents in the main extract b

Artificial neural network is a simulation of the biological nervous system. Its information processing function is determined by the input and output characteristics (activation characteristics) of the network Unit (neuron), the topology of the network (the connection mode of the neuron), the connection weight (synapti

Civilization number" and the Central State organ "youth civilization" title.Smart Apps Intelligent processing is the core problem 20w Human brain Power consumption Multilayer large-scale neural network ≈ convolutional Neural Network + LRM (different feature map extracts different features to complete

realization of Image search algorithm based on convolutional neural network If you use this name to search for papers, there must be a lot. Why, because from a theoretical point of view, convolutional neural networks are ideal for finding similar places in images. Think about it, a lot of Daniel, calf, and micro-ox articles are about how to find similar images fr

This paper study notes is their own understanding, if there are errors in the place, please correct criticism, common progress, thank you!Before the evaluation of teaching quality, only through the simple processing of teaching indicators, such as averaging or artificially given the weights of the indicators to sum weighted, the evaluation results with a great deal of subjectivity. Based on the BP neural network