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backprop.m
- this a program on matlab backpropagation of error.-this is a program on matlab backpropagation of error.
BACKPROP
- 用C++语言实现了B-P算法，隐含层节点多少仅受内存限制-Using C++ language to implement the BP algorithm, the number of hidden layer nodes limited only by memory
src_Backprop_ANN_XOR
- 人工神经网络ANN多层感知器BP神经网络算法的C#源代码，一个外国人编写，供大家研究学习使用。-ANN artificial neural network MLP BP neural network algorithm C# source code, a foreigner writing for everyone to learn.
backprop
- 神经网络程序,最速下降法，国外用c语言编写的。-bp net
backprop
- back prooagation neural networks
backprop
- 基于c语言的BP神经网络的实现，有中文注释，可供初学者参考-BP neural network based on the C language to achieve, Chinese Note for beginners
bpnn
- MATLAB neural network backprop code This code implements the basic backpropagation of error learning algorithm. The network has tanh hidden neurons and a linear output neuron. adjust the learning
backprop-code
- This A source code for Back-propagation neural networks-This is A source code for Back-propagation neural networks
backprop
- BP神经网络算法实现 批量处理的BP算法，隐层数为1 Z为特征向量组成的矩阵，n*m维，一行为一个样本 labZ为数据的类别，n*1维，每行对应样本的类别 M为隐层节点个数，T为迭代次数上限， epsilon为用于判断收敛的误差值，eta为学习率 W1为输入到隐层的权重，M*m维，B1为偏差，也就是乘以1的那个权重，M*1维 W2为隐层到输出的权重，c*M维，B2意义同B1，c*1维
MATLAB-neural-network-backprop-code
- this document describes how to use MLP neural network in Matlab
backprop
- 常用的模型和算法介绍,BP神经网络的C语言实现-The commonly used model and the algorithm is introduced, the BP neural network of C language implementation
backprop
- bp神经网络的算法实现，主要用于分类，检测等，很有效的方法。-bp neural network realization algorithm for classification and testing
backprop
- Neural network propagation program
deeplearing-hinton
- hinton2006年发表在science上的关于深度神经网络的文章Reducing the Dimensionality of Data with Neural Networks的matlab程序 mnistdeepauto.m //训练AutoEncoder的主文件 converter.m //将样本集从.ubyte格式转换成.ascii格式，然后继续转换成.mat格式 makebatches.m //创建小批量数
backprop
- 应用深度学习方法，挖掘数据在更深层次上的特征-Application of deep learning method, digging deeper level characteristic data
Backprop
- Back-propagation algorithm in neural network
App (2)
- Backpropagartion in java Neural Network