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1下载：
bayes和神经网络的手写体数字识别程序（matlab）(bayes and neural network handwritten numeral recognition program (matlab))
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tensorflow，简单神经网络识别手写字符(Recognition of handwritten characters by neural network)
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基于卷积神经网络的手写汉字识别_matlab版本_可以识别509类手写汉字(Based on the _ handwritten Chinese characters recognition _matlab version convolutional neural network can identify 509 kinds of handwritten Chinese characters)
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基于python3的利用神经网络进行的手写数字识别程序。(A handwritten numeric recognition program based on python3 based on Neural Network.)
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BP神经网络解决手写数字识别问题 matlab源代码(BP neural network solves the matlab source code for handwritten digit recognition problem)
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基于BP神经网络手写数字识别，main函数是主函数，识别的成功率在86%左右。(Based on the BP Neural Network handwritten digital recognition, the main function is the main function, and the recognition success rate is about 86%.)
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基于神经网络及GUI触摸板的手写数字识别，基本的机器学习例子(Handwritten numerals recognition based on neural network and GUI touch board, basic machine learning examples)
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基于概率神经网络的手写体数字识别，可用于课程设计(Handwritten digit recognition based on probabilistic neural network can be used for course design)
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下载MNIST数据集(手写体数字0-9)后，搭建卷积神经网络，将输入的数据集经过一层一层的卷积，到最后计算交叉熵，用梯度下降算法去优化它，使它变得最小，这就训练出了权重和偏置量，识别的准确率为91%(Download the MNIST data set (handwritten number 0-9), build a convolutional neural network, the input data set by convolutional layers, finally calcul
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用卷积神经网络实现手写数字识别，数据集为mnist数据集(Convolution neural network is used to realize handwritten numeral recognition. Data set is MNIST data set.)
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基于两层BP神经网络，加入dropout和softmax，输出层使用softmax，实现对手写字符库MNIST的识别，正确率达90%。(Based on the two level BP neural network, adding dropout and softmax, the output layer uses softmax to realize the recognition of handwritten character library MNIST, the accuracy ra
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使用BP神经网络实现手写字符库MNIST的识别。(The recognition of handwritten character library MNIST is realized by using BP neural network.)
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在anaconda+opencv+tensorflow平台下，利用简单的CNN卷积神经网络进行手写字符识别(Under the anaconda+opencv+tensorflow platform, we use simple CNN convolution neural network to handwritten character recognition.)
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手写体识别的训练，采用卷积神经网络，附带数据集下载代码(The training of handwritten recognition is based on convolution neural network, and the download from the dataset.)
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BP神经网络基本原理概述：这种网络模型利用误差反向传播训练算法模型，能够很好地解决多层网络中隐含层神经元连接权值系数的学习问题，它的特点是信号前向传播、误差反向传播，简称BP（Back Propagation）神经网络。BP学习算法的基本原理是梯度最快下降法，即通过调整权值使网络总误差最小，在信号前向传播阶段，输入信号经输入层处理再经隐含层处理最后传向输出层处理；在误差反向传播阶段，将输出层输出的信号值与期望输出信号值比较得到误差，若误差较大则把误差信号传回隐含层直至输入层，在各层神经元中使用
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卷积神经网络识别手写数字，放在jupyter直接跑，99%识别率，已经和Tensorboard联通好了(Convolutional neural network recognizes handwritten numerals and runs directly on jupyter. The recognition rate is 99%. It has been connected with Tensorboard.)
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LeNet神经网络 手写数字识别 下载数据集代码 数据集下载完成的(Handwritten Number Recognition Based on LeNet Neural Network)
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用tensorflow搭建卷积神经网络实现手写数据集的识别(Recognition of Handwritten Data Set by Constructing Convolutional Neural Network with Tenorflow)
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本课题为基于MATLAB的BP神经网络手写数字识别系统。带有GUI人机交互式界面。读入测试图片，通过截取某个数字，进行预处理，经过bp网络训练，得出识别的结果。可经过二次改造成识别中文汉字，英文字符等课题。(This project is based on Matlab bp neural network Handwritten digit recognition system. With GUI human-computer interactive interface. Read in the
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该课题为基于MATLAB bp神经网络的手写汉字识别系统。可以利用鼠标手写中文汉字进行训练，测试，可以识别任何字体，只需要到GUI界面面板更换即可。在GUI界面就可以随写随训练中文，不需要到后台手动更换文字训练，方便快捷上档次。(This project is a handwritten Chinese character recognition system based on Matlab bp neural network. You can use the mouse to write Ch
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