搜索资源列表
CUDA-CNN-master
- CNN cuda的加速。 start-of-art结果的流行的数据集 1。测试mnist并获得99.76 ,投票后(99.82 )(最好的99.79 ) 2。测试cifar-10并获得81.42 最好(90 ) 3。测试cifar - 100和51.13 (最好的65 )-CNN accelerated by cuda. The start-of-art result s of popular datasets 1. Test on mnist and get 99.76
hw4
- k近邻分类，内部有文档详细说明，具体例子是通过k近邻对常用数据集mnist.mat（头像数据）进行分类-k nearest
CUDA-CNN-master
- 提升mnist字符识别准确度，利用cuda进行加速其识别过程-improve the accuracy of mnist
MNIST_theano
- 利用theano库完成MNIST手写识别，包括稀疏自编码机，多层感知机，卷积神经网络-using the theano to complete the handwriting congnization in MNIST ,include Denoising AutoEncoder,MLP,Convolution Neutral Network.
CNNfor-handwriting-Mnist
- 能够实现对手写字符的识别，是一个简单的系统，功能齐全-To achieve recognition of handwritten characters, it is a simple system, fully functional
mycnn
- 卷积神经网络识别字符的Matlab程序，包含所需的所有素材和自己改进的一部分代码-Convolutional neural network for handwriten digits recognition: training and simulation. This program implements the convolutional neural network for MNIST handwriten digits recognition, created by Yan
readMNIST
- 对于MNIST图像数据库，利用该M文件读出，方便后续进行实验。-The function ReadMnist was programmed to solve the problem of read information the MNIST .
cnn_vs2012
- 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
CNN2
- 基于Mnist库的手写数字识别的C++源代码，用CNN实现，并且建立了用户界面-Handwritten numeral recognition Mnist library C++ source code, using CNN achieve, and the establishment of a user interface
dataset_mnist
- 压缩包中包含完整的mnist数据集及相应的bmp格式的图像数据-Compressed image data package contains the complete data set and the corresponding mnist bmp format
CNN
- 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
93131057-Naderi
- MLP matlab code for mnist dataset.
MNIST-OCR-ELM
- 多隐含层极限学习机，适合对大数据进行处理-Multiple hidden layer limit learning machine, suitable for large data processing
Lenet
- 这个资源使用实现lenet-5的网络结构来MNIST数据集，代码参考了UFLDL上的相关的代码，以及R. B. Palm实现的CNN中的相关代码，为了适应数据集我把lenet-5输入的大小改为了28*28，c3的每一张特征图都与s4的每一张特征图相关，训练的结果可以达到99.1 -The resources for network structure lenet-5 to MNIST data sets, code reference to the relevant code UFLDL on
lenet5test
- 实现lanet卷积，进行手写体识别。数据源可以来自mnist-Achieve lenet convolution, handwritten recognition. The data source can come mnist
CNN-MINIST
- 利用卷积神经网络进行MINIST数据集的分类识别，MATLAB源程序。-Convolution neural network classification MNIST dataset, MATLAB source.
mnist_train
- hebrew characters in mnist format
neural-network
- 深度学习python实现，并附有MNIST上的测试程序，准确率98 以上-Deep learning learns low and high-level features large amounts of unlabeled data, improving classification on different, labeled, datasets. Deep learning can achieve an accuracy of 98 on the MNIST dataset.
convolution-nn
- 卷积神经网络源代码，visual studio可以运行的！mnist手写库的idx-ubyte格式的数据在里面，原图片再另一个工程里-convolution neural network
KNN
- KNN算法练习，使用mnist数据库，在包里已经集成好，包含knn算法和数据库，可直接使用-KNN,you can learn this codes