recurrent convolutional neural networks

The accuracy of the mnist test set is about 90% and 96%, respectively, for single-layer neural networks and multilayer neural networks in the previous two essays. The correct rate has been greatly improved after the multi-layer neural network has been swapped. This time the

ResNet, AlexNet, Vgg, inception:understanding various architectures of convolutional Networksby koustubh This blog from: http://cv-tricks.com/cnn/understand-resnet-alexnet-vgg-inception/ 　　　　 convolutional neural Networks is fantastic For visual recognition Tasks.good convnets is beasts withmillions of parameters and

minute, the reporters can automatically make the recording of the record ... There are more words in a person's life than we have written, and if there is a software that can record all the words we have said and manage efficiently, how incredible the world will be. Acoustic modeling technology based on DFCNNAcoustic modeling of speech recognition is mainly used to model the relationship between voice signals and phonemes, and Iflytek, as a framework for acoustic modeling, was proposed last Dec

Visual comprehension of convolutional neural networks (visualizing and understanding convolutional Networks)Summary (abstract)Recently, the large convolutional neural network model has

, the objective function of SVM is still convex. Not specifically expanded in this chapter, the seventh chapter is detailed.Another option is to fix the number of base functions in advance, but allow them to adjust their parameters during the training process, which means that the base function can be adjusted. In the field of pattern recognition, the most typical algorithm for this method is the forward neural network (Feed-forward

Very Deep convolutional Networks for large-scale Image recognition reprint please specify: http://blog.csdn.net/stdcoutzyx/article/ details/39736509 This paper is in September this year's paper [1], a relatively new, wherein the point of view felt for convolutional neural network parameter adjustment has a gre

After figuring out the fundamentals of convolutional Neural Networks (CNN), in this post we will discuss the algorithm implementation techniques based on Theano. We will also use mnist handwritten numeral recognition as an example to create a convolutional neural network (CN

CNN (convolutional neural Network)Convolutional Neural Networks (CNN) dating back to the the 1960s, Hubel and others through the study of the cat's visual cortex cells show that the brain's access to information from the outside world is stimulated by a multi-layered recepti

Very Deep convolutional Networks for large-scale Image recognition1. Major contributions This paper explores the change of the effect of CNN as the number of layers increases as the number of parameters is basically unchanged. (thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, Which shows

Very Deep convolutional Networks for large-scale Image recognition Reprint Please specify:http://blog.csdn.net/stdcoutzyx/article/details/39736509 This paper is in September this year's paper [1], relatively new, in which the views of the convolution neural network to adjust the parameters of a great guide, a special summary. About

that the 2D convolution is actually 3D (the dimensions of the convolution kernel should be kernel_height * kernel_height * input_channel), except that the third dimension is exactly equal to the number of input channels, So the volume after the third dimension on the lost, became a flat two-dimensional feature map, so called 2D convolution.Another way of understanding is that the shape of a convolution core is kernel_height * kernel_height, and there is a input_channel layer, the process of mak

The mnist examples of convolutional neural networks and the neural network examples in the previous blog post are mostly the same. But CNN has more layers, and the network model needs to be built on its own.The procedure is more complicated, I will be divided into several parts to describe.First, download and load the

This note describes the third week of convolutional neural networks: Target detection (1) Basic object detection algorithmThe main contents are:1. Target positioning2. Feature Point detection3. Target detectionTarget positioningUse the algorithm to determine whether the image is the target object, if you want to also mark the picture of its position and use the b

:　　　　　　　　　　　　　　　Now we can easily pair the derivative of the training bias and displacement bias:　　　　　　　　　　　　　　　　　　 　　　　　　　　　　　　　　The most important step is to solve the error term (also known as sensitivity), the other calculations are based on this. The solution of the error term is first to analyze which node J needs to be computed and which nodes of the next layer are related, because node J affects the final output through the next layer of neurons connected to the node, which also requires

achieved the ImageNet 6.67% the results.2.2 Inception V2Inception V2 learned that the Vgg used two 3′3 convolution instead of the large convolution of 5′5, and built more nonlinear transformations while reducing the parameters, making CNN more capable of learning features:Two 3′3 convolution layer functions similar to a 5′5 convolution layerIn addition, the famous Batch normalization(hereinafter referred to as BN) method is proposed. BN is a very effective regularization method, which can accel