用java实现卷积神经网络，平台是eclipse，如何用eclipse导入可以参考http://blog.csdn.net/baidu_37107022/article/details/70209949，作者是http://www.cnblogs.com/fengfenggirl

卷积神经网络matlab版源码，实现对手写体图片的数字识别。实验所需数据可到https://download.csdn.net/download/u013479571/10664562下载

该压缩包包括了TensorFlow基于CIFAR10数据集的卷积神经网络的代码实现，以及多个测试结果的测试图片。

python实现的卷积神经网络CNN,无框架，python实现的卷积神经网络CNN,无框架

卷积神经网络CNN进行图像分类

介绍了利用卷积神经网络进行遥感解译的方法与过程，是不错的资源

本文档从最基础的原理着手，介绍了使用CNN卷积神经网络进行图片分类，是利用深度学习通过卷积神经网络进行图片分类比较不错的参考资料。

卷积神经网络CNN进行图像分类

利用卷积神经网络（CNN）对高光谱图像进行分类，内含高光谱数据

深度学习之卷积神经网络CNN做手写体识别的VS代码。支持linux版本和VS2012版本。 tiny-cnn: A C++11 implementation of convolutional neural networks ======== tiny-cnn is a C++11 implementation of convolutional neural networks. design principle ----- * fast, without GPU 98.8% accuracy on MNIST in 13 minutes training (@Core i7-3520M) * header only, policy-based design supported networks ----- ### layer-types * fully-connected layer * convolutional layer * average pooling layer ### activation functions * tanh * sigmoid * rectified linear * identity ### loss functions * cross-entropy * mean-squared-error ### optimization algorithm * stochastic gradient descent (with/without L2 normalization) * stochastic gradient levenberg marquardt dependencies ----- * boost C++ library * Intel TBB sample code ------ ```cpp #include "tiny_cnn.h" using namespace tiny_cnn; // specify loss-function and optimization-algorithm typedef network CNN; // tanh, 32x32 input, 5x5 window, 1-6 feature-maps convolution convolutional_layer C1(32, 32, 5, 1, 6); // tanh, 28x28 input, 6 feature-maps, 2x2 subsampling average_pooling_layer S2(28, 28, 6, 2); // fully-connected layers fully_connected_layer F3(14*14*6, 120); fully_connected_layer F4(120, 10); // connect all CNN mynet; mynet.add(&C1); mynet.add(&S2); mynet.add(&F3); mynet.add(&F4); assert(mynet.in_dim() == 32*32); assert(mynet.out_dim() == 10); ``` more sample, read main.cpp build sample program ------ ### gcc(4.6~) without tbb ./waf configure --BOOST_ROOT=your-boost-root ./waf build with tbb ./waf configure --TBB --TBB_ROOT=your-tbb-root --BOOST_ROOT=your-boost-root ./waf build with tbb and SSE/AVX ./waf configure --AVX --TBB --TBB_ROOT=your-tbb-root --BOOST_ROOT=your-boost-root ./waf build ./waf configure --SSE --TBB --TBB_ROOT=your-tbb-root --BOOST_ROOT=your-boost-root ./waf build or edit inlude/co