搜索资源列表
Demo-Mnist
- 基于神经网络的手写数字识别的源代码，绝对能够正常编译并运行！-based on neural network handwritten numeral recognition of the source code is absolutely normal to compile and run!
Handwritten-Character
- 基于CNNs的手写字符识别系统，载入MNIST手写字符数据库，通过训练提取特征，达到99 的识别率-Based on CNNs handwritten character recognition system, load MNIST handwritten character database, extract features through training, up to 99 recognition rate
SvmMNIST
- 通过SVM算法识别MNIST手写数字库，并加入了一些预处理算法，包括数字图像的大小调整归一化等，效果不错。-By SVM algorithm identifies MNIST handwritten digital library and added some preprocessing algorithms, including the size of the digital image adjustment normalized so good results.
MNIST
- 这个压缩包，是一个手写数字识别库，世界上最权威的，美国邮政系统开发的，可以作为标准的数据集合使用测试分类器-This compression package, is a handwritten numeral recognition , the world' s most authoritative, the U.S. postal system developed can be used as a standard data set using the test classifier
Demo-MNist
- 利用神经网络进行手写数字识别演示代码！非常具有代表性！-Using neural network Digital Recognition demo code!
lenet_iter_10000
- caffe-windows mnist 训得到的模型，可用于手写字体识别(Caffe-windows MNIST training model can be used for handwritten font recognition)
BP_mnist
- BP网络实现手写字体识别。压缩文件包含mnist数据集，直接在pycharm运行BPNetwork.py文件，输出测试集识别结果和测试精度。(Handwritten recognition based on BP network. The compressed file contains the MNIST data set, runs the BPNetwork.py file directly in the pycharm, outputs the test set, identifies
code&doc
- 基础的卷积神经网络代码，实现mnist手写字符识别，含中文文档说明(Basic CNN code, including detailed annotation in Chinese)
Handwritten_numeric_recognition
- 基于keras深度学习框架的手写数字字符识别(Handwritten numeric character recognition based on keras depth learning framework)
fisher
- 利用fisher方法实现手写体数字多分类识别，采用mnist数据集(simple program using fisher)
least_square
- 利用最小二乘法实现手写体数字识别，采用mnist数据集(simple program using least-square)
mnist.pkl
- mnist数据集，用于手写数字识别的数据集，机器学习入门必备(mnist data,original data in http://yann.lecun.com/exdb/mnist/)
mnist
- 手写数字识别。通过各种数字图片进行机器识别，属于机器学习入门级别编程。(Handwritten digit recognition. The machine is recognized by various digital pictures, which belongs to the introduction level programming of machine learning.)
纯C-CNN
- 纯C深度学习库，里面包含MNIST手写数字识别数据集，编译就能训练和预测(Pure C depth learning library, which contains MNIST handwritten digital recognition data sets, compiling can be trained and predicted.)
chapter19
- 在MATLAB平台上的基于svm的手写数字体识别(Handwritten numeral recognition based on svm)
python-dbn-master
- 运用python语言，基于dbn的手写数字体识别(Handwritten numeral recognition based on dbn using python language)
mnist
- 利用keras实现手写数字识别，使用CNN模型 全连接层+两个卷积层，最后Softmax分类器，识别率超过96%(Using keras to realize handwritten numeral recognition baesd on CNN model. One whole connection layer + two convolution layers, and a Softmax classifier. The recognition accuracy is over 96%
code(BP_to_MNIST)
- 使用BP神经网络实现手写字符库MNIST的识别。(The recognition of handwritten character library MNIST is realized by using BP neural network.)
input_data
- mnist数据集的导入文件，官网上有可能进不去(def maybe_download(filename, work_directory): """Download the data from Yann's website, unless it's already here.""")
神经网络-手写数字识别
- 利用BP神经网络，对MNIST数据集中的5000张图片进行训练，实现手写数字识别，训练出来的结果准确率在90%。