creating neural network

Recently, the journal Platform Distill published an article by Google researchers, introducing a powerful tool for neural network visualization and style migration: micro-image parameterization. This article describes the tool in several ways. Image Classification Neural network has excellent image generation capa

Why should I introduce an activation function?If you don't have to activate the function (actually equivalent to the excitation function is f (x) =x), in this case you each layer of output is a linear function of the upper input, it is easy to verify that no matter how many layers of your neural network, the output is a linear combination of input, and no hidden layer effect, this is the most primitive perc

This paper, based on the http://en.wikipedia.org/wiki/Backpropagation of Wikipedia, makes a summary of the neural network's back propagation algorithm, and makes a simple formula derivation.A typical post-propagation algorithm for a 3-layer neural network with only 1 hidden layers is as follows:Initialize network weigh

catalog view Summary view Subscription [Top] "convolutional neural network-evolutionary history" from Lenet to AlexnetTags: CNN convolutional neural Network Deep learningMay 17, 2016 23:20:3046038 people read Comments (4) favorite reports Classification:"Machine Learning Deep Learning" (a)Copyright NO

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

The ① Artificial Neural Network (ANN) is a widely connected giant system. Neuro-scientific research shows that the main part of the human central nerve cortex is composed of 10[11]~10[12] neurons, each neuron has a 10[1]~10[5] synapse, Synapse is a junction between neurons, determining the strength and nature of the connection between neurons. This suggests that the cerebral cortex is an extensively connect

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Cost Function ( Cost functions ) The last lecture introduced the multiclass classification problem. The difference between the multiclass classification problem and the binary classification problem lies in that there are multiple output units, which are summarized as follows: At the same time, we also know the price functions of Logistic regression as follows: The first half repres

What's RNN? The cyclic neural network, the recurrent neural network, is proposed mainly to deal with sequence data and what sequence data is. is the previous input and the back of the input is related, such as a word, before and after the words are related, "I am hungry, ready to go to XX", according to the input of t

In this paper, a simple handwriting recognition system is realized by BP neural network.First, the basic knowledge1 environmentpython2.7Need to numpy and other librariesCan be installed with sudo apt-get install python-2 Neural Network principleHttp://www.hankcs.com/ml/back-propagation-neural-network.htmlIt is particul

AlexNet: (ILSVRC Top 5 test error rate of 15.4%) the first successful display of the convolutional neural network potential network structure. key point: with a large amount of data and long-time training to get the final model, the results are very significant (get 2012 classification first) using two GPU, divided into two groups for convolution. Since Alex

BP (back propagation) neural network was proposed by the team of scientists led by Rumelhart and McCelland in 1986, which is one of the most widely used neural network models, which is a multilayer Feedforward network trained by error inverse propagation algorithm. The BP

BP (back propagation) network is the 1986 by the Rumelhart and McCelland, led by the team of scientists, is an error inverse propagation algorithm training Multilayer Feedforward Network, is currently the most widely used neural network model. BP network can learn and store

Python implements basic model of a single hidden layer Neural Network As a friend, I wrote a python code for implementing the Single-hidden layer BP Ann model. If I haven't written a blog for a long time, I will send it by the way. This code is neat and neat. It simply describes the basic principles of Ann and can be referenced by beginners of machine learning. Several important parameters in the model: 1.

Neural NETWORKS, part 3:the NETWORKWe have learned on individual neurons in the previous section, now it's time to put them together to form an actual neu RAL Network.The idea was quite simple–we line multiple neurons up to form a layer, and connect the output of the first layer to the I Nput of the next layer. Here are an illustration:Figure 1:neural the network

The previous article mentions the difference between data mining, machine learning, and deep learning: http://www.cnblogs.com/charlesblc/p/6159355.htmlDeep learning specific content can be seen here:Refer to this article: Https://zhuanlan.zhihu.com/p/20582907?refer=wangchuan "Wang Chuan: How deep is the depth of learning, how much did you learn?"(i) "Note: Neural network research, because the artificial int

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 receptive Field. On the basis of feeling wild, 1980 Fukushima proposed a theoretical model Neocog