搜索资源列表
Gabor
- 自己写的gabor小波卷积变换，8位图，学习模式识别有用的gabor-Gabor wrote it myself convolution wavelet transform,
GrowthtypeConvolutionNeuralNetworkandItsApplicatio
- 增长式卷积神经网络及其在人脸检测中的应用-Growth-type Convolution Neural Network and Its Application in Face Detection growth-type convolution neural network and its application in face detection growing type convolution neural network and its application in face detecti
CNN.tar
- 卷积神经网路 包括了MLP层，conv层，pooling层，RBM层，LRN层，采用xml配置文件设置参数，实现训练识别-Convolution neural network, including the MLP layer, conv layer, pooling layer, RBM layer, LRN layer, using xml configuration file to set parameters to achieve recognition training
CNN
- 用MATLAB实现卷积神经网络，并对图像进行特征提取-Using MATLAB convolution neural networks, and image feature extraction
conv-net-0.1
- 这是卷积神经网络在字符识别的应用，大家可以学习学习。-This is a convolution neural network character recognition application, we can learn it.
CNNdigitrecognize
- CNN卷积神经网络数字识别代码，运行后有很友好的识别界面-CNN convolution neural network digital identification code, after running very friendly recognition interface
juanjishenjingyuanjingdianlicheng
- 卷积神经元的经典历程，它其中包括了卷积神经元的运行数据库以及运行的代码，里面带有讲解，对初学者很有帮助。-The classic course of the convolution of neurons, which includes the operation of the and the operation of a convolution of the code, which carries on to explain, very helpful for beginners.
tiny-dnn-master
- 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
CNN-pooling-strategy
- 基于卷积层和池化层的卷积深度网络被执行，该框架可以有效地识别灰度图像，彩色图像和高光谱图像。- Convolution deep network based on convolution layer and pooling layer is performed, the framework can effectively identify grayscale images, color images and hyperspectral images.
SAE_DBN_CNNToolbox
- 多种深度学习框架，主要包括堆栈稀疏自动编码器，深信度网络，卷积神经网络等。对于灰度图像和高维图像，展现非常强大的学习性能。-A variety of deep learning framework, including automatic stack sparse encoder, is convinced of the network, convolution neural networks. For grayscale images and high-dimensional image, s
CNN
- 用卷积神经网络实现的手写数字识别（minst）,可直接运行，识别率较高。用Tensorflow实现-Handwritten digital recognition (minst) with convolution neural network can be run directly and the recognition rate is high. Implemented with Tensorflow
Handwriting-recognition-algorithm
- 基于卷积神经网络的手写体识别算法，测试数据和训练数据都有，笨人已经检验过，很好用-Handwriting recognition algorithm based on the convolution neural network
keras_mnist_test
- hello Word of keras ,第一个成功实现的卷积神经网络,下载了mnist数据集,并decode,,然后,为了加快速度,训练其中的一部分数据,并用predict测试,ok,2min内出结果.(网上其它程序试过,训练太久,一晚上都没训练出结果,于是自己动手设计了这个小程序) 环境:python3.6,keras2.1,PC i5(很破的电脑)(Hello Word of keras, the first successful convolution neural network,
cnn
- 基于python tensorflow框架构建的卷积神经网络用来识别图像，附带训练数据集的制作代码。(The convolution neural network based on the python tensorflow framework is used to identify images with the production code of the training data set.)
machine learning
- 反向传播算法与利用卷积神经网络识别手写体(Back propagation algorithm and recognition of handwriting by using convolution neural network)
cnn人脸识别
- 用卷积神经网络做的人脸识别，准确率达到92.75%。(The accuracy of face recognition using convolution neural network is 92.75%.)