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matlab版的卷积神经网络，学习和交流使用.matlab版的卷积神经网络，学习和交流使用-convolutional neural network of matlab for research
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Convolutional neural network
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卷积神经网络是人工神经网络的一种，已成为当前语音分析和图像识别领域的研究热点。它的权值共享网络结构使之更类似于生物神经网络，降低了网络模型的复杂度，减少了权值的数量。自己可已加入数据，和修改梯度。-Convolutional neural network is an artificial neural network, it has become a research hotspot speech analysis and image recognition field. It weights
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卷积神经网络的卷积实现，可以实现，大家可以试试，导入Theao的库文件。-Convolutional neural network
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卷积神经网络，用于图像的识别分类，对图像的旋转平移，畸变具有很强的容忍性。-Convolutional neural network for image recognition and classification, has a strong effect
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卷积神经网络的实现，未使用matlab工具包函数-Convolutional neural network implementation, not using the MATLAB toolkit function
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一个简单的CNN的matlab实现，用于分割脑血管(a simple implementation of convolutional neural networks and just for the Segmentation of Brain Vessels)
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卷积神经网络代码齐全，手写数字识别以及SAE的例子(Convolutional neural network code)
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A Deep Convolutional Neural Network for Background Subtraction
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CNN - Convolutional neural network
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CNN - Convolutional neural network for brain tumor
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CNN convolutional neural network
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这是一个经过自己整理的卷积神经网络的代码，对0-9数据集进行分类的代码，使用tensorflow框架完成的，主要使用的语言是Python，可以直接运行，初学者可以用于学习交流。(This is a self compiled convolutional neural network code, 0-9 data sets for classification of code, using the tensorflow framework to complete, the main languag
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实现卷积神经网络与支持向量机的联合调试使用(Joint debugging and implementation of convolutional neural network and support vector machine)
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卷积神经网络（lenet_5）代码,用于手写字符识别(code of convolutional neural network for handwork characterisic recognation)
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Convolutional Neural Network
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Scalability Approach to Convolutional Neural Networks
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Variety Identification of Single Rice Seed Using
Hyperspectral Imaging Combined with
Convolutional Neural Network
Zhengjun Qiu 1,2, Jian Chen 1,2, Yiying Zhao 1,2, Susu Zhu 1,2, Yong He 1,2 ID and
Chu Zhang 1,2,*
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It is slightly simplified implementation of Kim's [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) paper in Tensorflow.
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人体骨骼的关键点提取，总共十四个点，使用卷积神经网络。(The key points of human skeleton are extracted from a total of fourteen points using convolutional neural networks.)
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