搜索资源列表
DeepLearnToolbox-master
- 深度学习代码,有各种深度学习的模型。有卷积神经网络,稀疏自动编码-it is a DeepLearnToolbox,we can see cnn,sae,dbn,sae in this zip.
CNN
- 这是个卷积神经网络的实现代码,对手写体进行识别,现在正确率可以达到90 -This is a convolution neural network implementation code of conduct handwriting recognition accuracy rate is now 90
cnncoder
- 深度学习中的卷积神经网络代码,包括前向后向反馈以及训练应用等-caffe deep learning
CNN
- 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
Training-code-for-SRCNN
- 超分辨卷积神经网络,深度学习,MATLAB代码,获取深度学习的训练数据-Super-resolution convolution neural network, the depth of learning, MATLAB code, get deep learning of the training data
DeepLearnToolbox-master
- 可以进行深度学习,卷积神经网络的一种开源代码,可以对图像数据库自动提取特征(You can do deep learning)
CNN_code
- 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)
cnn
- 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
Lenet
- 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)
111.rar
- 有关于直接可运行卷积神经网络的各类matlab源代码(The matlab code of the convolution neural network can directly run the matlab code of the convolution neural network,)
卷积神经网络CNN代码解析-matlab
- 卷积神经网络CNN的代码解析文档,可以辅助了解CNN的MATLAB程序实现过程(CNN code analysis document of convolution neural network)
Deep learning_CNN DBN RBM
- 运用深度学习模型实现图像的分类,主要包括卷积神经网络CNN和深信度网络DBN(Classification of images using deep learning model includes convolutional neural network CNN and belief network DBN.)
卷积神经网络源码-深度
- 卷积神经网络源码,学习用代码,内容较好。(Convolutional Neural Network Source Code)
CFICA_11
- 盲分离代码,是实现卷积神经网络的盲分离matlab代码,其中附带有原理及其论文,是盲信号分离技术的一个好例子(Matlab code for blind separation of convolutional neural networks. Principles and papers are attached. It is a good example of blind signal separation technology.)