convolutional neural network tutorial

http://blog.csdn.net/diamonjoy_zone/article/details/70576775Reference:1. inception[V1]: going deeper with convolutions2. inception[V2]: Batch normalization:accelerating deep Network Training by reducing Internal covariate Shift3. inception[V3]: Rethinking the Inception Architecture for computer Vision4. inception[V4]: inception-v4, Inception-resnet and the Impact of residual Connections on learning1. PrefaceThe NIN presented in the previous article ma

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

Vggnet　Vggnet is a deep convolutional neural network developed by the computer Vision Group of Oxford University and a researcher at Google DeepMind. Vggnet explores the relationship between the depth of convolutional neural networks and their performance, and vggnet success

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

the local feature is extracted, the position relationship between it and other features is determined; s layer is the feature map layer, and each computing layer of the network is composed of multiple feature mappings. Each feature is mapped to a plane, and the weights of all neurons on the plane are equal. The feature mapping structure uses the sigmoid function which affects the function core as the activation function of

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 border marked outAmong the problems we have st

similar to the dimensionality reduction) method. Maximum pooling divides the input image into overlapping image matrix blocks, and each sub-region outputs its maximum value. The two reasons why the maximum pooling method is very effective in the visual processing problem are:(1) Reduce the computational complexity of the upper level by reducing the non-maximum value.(2) The result of pooling supports translation invariance. In the convolution layer, each pixel point has 8 orientations that can

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 pa

The first part of the full-connected network weights updateconvolutional neural network using gradient-based learning methods to supervise training, in practice, the general use of random gradient descent (machine learning in several common gradient descent) version, for each training sample is updated once the weight, error function using the error square Sum fu