Neural network

【面向代码】学习 Deep Learning（三）Convolution Neural Network(CNN) - DarkScope从这里开始. 最近一直在看Deep Learning，各类博客、论文看得不少 但是说实话，这样做有些疏于实现，一来呢自己的电脑也不是很好，二来呢我目前也没能力自己去写一个toolbox 只是跟着Andrew Ng的UFLDL tutorial 写了些已有框架的代码(这部分的代码见github) 后来发现了一个matlab的Deep Learning的toolbox，发现其代码很简单，感觉比较适合用来学习算法 再一个就是matlab的实现可以省略掉很多数据结构的代码，使算法思路非常清晰 所以我想在解读这个toolbox的代码的同时来巩固自己学到的，同时也为下一步的实践打好基础 (本文只是从代码的角度解读算法，具体的算法理论步骤还是需要去看paper的 我会在文中给出一些相关的paper的名字，本文旨在梳理一下算法过程，不会深究算法原理和公式) 使用的代码：DeepLearnToolbox ，下载地址：点击打开，感谢该toolbox的作者 今天是CNN的内容啦，CNN讲起来有些纠结，你可以事先看看convolution和pooling(subsampling)，还有这篇：tornadomeet的博文 下面是那张经典的图： 打开\tests\test_example_CNN.m一观 cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer }; cnn = cnnsetup(cnn, train_x, train_y); opts.alpha = 1; opts.batchsize = 50; opts.numepochs = 1; cnn = cnntrain(cnn, train_x, train_y, opts);

Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background History Farley and Wesley A.

Models or both. -Artificial Neural Network- Matlab 操作介紹 - 以類神經網路 BPN Mo... 93-Matlab6.5-BPN%20Model. Neural network matlab - Google j M. Neural network - Google j M. Matlab 課程 教學資源. Neural 課程 教學資源 【按右鍵下載】 ＊ 賺錢不容易，寫書肯定更難。

Neural Network Toolbox supports supervised learning with feedforward, radial basis, and dynamic networks. It also supports unsupervised learning with self-organizing maps and competitive layers. With the toolbox you can design, train, visualize, and simulate neural networks. You can use Neural Network Toolbox for applications such as data fitting, pattern recognition, clustering, time-series prediction, and dynamic system modeling and control. To speed up training and handle large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox™.

Getting Started with Neural Network Toolbox - MATLAB Vidéo - MathWorks France.