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这是利用神经网络来实现手写字符识别，起准确率已经达到99.26 ，可以继续调整参数达到更深层次的效果。需要自己搭建opencv环境。后期工作可以利用cuda对其更深层次的加速-This is achieved using a neural network handwritten character recognition, since the exact rate has reached 99.26 percent, can continue to adjust the parameters t
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基于CNN的手写文本识别，卷积神经网络模型，并受到深度学习中预训练方式的启发，提出一种类别累加的训练方式，采用这种类别累加方式进行卷积神经网络模型的训练-Handwritten text recognition based on CNN neural network model, convolution, and inspired by the pre training in depth study, put forward a kind of category cumulative train
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用于手写数字的识别，用Matlab编程，里面有数字样本和程序，用神经网络进行训练，已经运行成功。-For recognition of handwritten digits, Matlab programming, which has digital samples and procedures for training the neural network, has been run successfully.
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基于概率神经网络的手写体数字识别，matlab实现，非常好的程序-Handwritten numeral recognition, probabilistic neural network based on MATLAB, a very good program
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基于Mnist库的手写数字识别的C++源代码，用卷积神经网络实现-Handwritten numeral recognition Mnist library C++ source code, using convolution neural network
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用 卷积神经网络进行手写字符 识别,内含mnist训练集-Handwritten character recognition, containing mnist convolution neural network training set
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手写体数字的识别，采用bp神经网络，有很好的效果-Recognition of handwritten digits, using bp neural network, the results were OK
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这是一个特别有用的手写数字识别方法，基于bp神经网络做法，识别率接近100 ，因为没有什么商业价值分享了-This is a particularly useful method of handwritten digit recognition based on bp neural network, the recognition rate of nearly 100
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用VS2012实现手写数字识别的卷积神经网络算法，用mnist库作为输入-Using VS2012 to achieve the handwritten numeral recognition of the convolution neural network algorithm, using the MNIST library as an input
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此代码是对卷积神经网络用于手写字体识别的实现，程序是基于theano库开发的，并且用到了集成化模块keras,方便我们构建自己的网络结构，很好的解决分类问题-This code is the convolution neural network for handwritten character recognition, the program is based on the theano library development, and use the integrated modular k
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深度信念网络 (Deep Belief Network, DBN) 由 Geoffrey Hinton 在 2006 年提出。它是一种生成模型，通过训练其神经元间的权重，我们可以让整个神经网络按照最大概率来生成训练数据。我们不仅可以使用 DBN 识别特征、分类数据，还可以用它来生成数据。下面的图片展示的是用 DBN 识别手写数字： -Depth belief networks (Deep Belief Network, DBN) proposed by the Geoffrey Hinton i
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基于概率神经网络的手写体数字识别，程序可以运行，会显示识别率百分比-Handwritten digit recognition based on probabilistic neural network, the program can run, it displays the percentage recognition rate
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BP神经网络，用于手写数字的识别，非常实用，可以直接运行。-The BP neural network to handwritten digital recognition, very practical, can be directly run.
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数字图像处理，基于BP神经网络识别手写数字-Digital image processing, BP neural network based recognition of handwritten numbers
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BP神经网络识别手写字符验证码，包括10721张字母、数字样本-BP neural network handwritten character recognition codes, including 10,721 letters, numbers, samples
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用matlab实现的基于概率神经网络的手写体数字识别程序，这是一个概率神经网络的实际应用-Using matlab to achieve based on probabilistic neural network handwritten numeral recognition program, which is the practical application of a probabilistic neural network
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《MATLAB神经网络原理与实例精解》中chap13的例子 基于概率神经网络的手写体数字识别-" MATLAB network principles and examples of fine nerve Solutions" in the example chap13- Based Probabilistic Neural Network handwritten numeral recognition
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cnn卷积神经网络实现mnist的手写体识别程序-CNN convolution neural network to realize mnist handwritten recognition program
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基于BP神经网络的手写数字识别，总共有三个程序，分别是建立样本，进行训练，最后是检验测试-Based on the BP neural network handwritten numeral recognition, a total of three procedures, namely, the establishment of samples, training, and finally test
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基于matlab利用BP神经网络开发的手写数字识别，正负样本为分别为1000张，手写数字是minist库-Based on BP neural network matlab developed handwritten numeral recognition, positive and negative samples were 1000, handwritten digital library is minist
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