搜索资源列表
MINIST
- mnist库上 应用DBN网络 DBN使用RBM结构，半监督网络，逐层训练(Application on the DBN network)
Lenet
- 经典五层卷积神经网络，应用在mnist库上(The classical five layer convolution neural network application in the MNIST database)
5-4 tensorboard visualization
- tensorflow手写数字识别学习，根据样本进行计算(learn TensorFlow with mnist)
stacking
- kaggle digitrecognizer MNIST by stacking some machine learning method, such like GBM(Gradient Boosting Method), LR, Extra Randomized Trees, Random Forest,KNN,etc.用stacking的方法实现手写数字识别MNIST。(kaggle digitrecognizer MNIST by stacking some machine learnin
nn_code
- 使用Python实现的一些简单神经网络算法，实现的神经网络包括BP,CNN,RNN,LSTM等，主要是理解这些神经网络的算法原理，并附有mnist数字识别例子。(neural network,include BP,CNN,RNN,LSTM.)
neural-networks
- 神经网络和深度学习的代码 ，内含有一个mnist手写字识别的源码(Code for neural networks and deep learning, containing source code identified by a MNIST handwritten word)
test1
- 神经网络，深度学习上非常经典的例子-RNN循环神经网络，使用mnist数据集，代码简单易懂，学习方便(Neural network, deep learning is a very classic example -RNN circular neural network, the use of mnist data sets, the code is easy to understand, easy to learn)
cnn_mnist
- CNN over Mnist Dataset
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.)
Extended Yale B Database
- 这是MNIST数据库（一个手写数字的数据库，它提供了六万的训练集和一万的测试集，它的图片是被规范处理过的，28*28的灰度图） 总共4个文件: train-labels-idx1-ubyte: training set labels t10k-images-idx3-ubyte:? test set images t10k-labels-idx1-ubyte:? test set labels train-images-idx3-ubyte: training set images(T
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.)
lib.tar
- 将训练之后的caffe model生成.so库文件，方便在工程中直接调用，不需要写caffe test分代码(he Caffe model after the training is generated for the.so library file, which is convenient to call in the project and does not need to write Caffe test code)
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)
Run_MNIST
- 下载MNIST数据集(手写体数字0-9)后，搭建卷积神经网络，将输入的数据集经过一层一层的卷积，到最后计算交叉熵，用梯度下降算法去优化它，使它变得最小，这就训练出了权重和偏置量，识别的准确率为91%(Download the MNIST data set (handwritten number 0-9), build a convolutional neural network, the input data set by convolutional layers, finally calcul
my_cnn.tar
- 用卷积神经网络实现手写数字识别，数据集为mnist数据集(Convolution neural network is used to realize handwritten numeral recognition. Data set is MNIST data set.)
MNLIST and CNN
- 实现了在Mnist上的分类，使用了卷积神经网络(use convoluntional neural network to implement classificaiton on Minist.)
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%
rasmusbergpalm-DeepLearnToolbox-5df2801
- mnist数据库，可用matlab运行，学习神经网络(MNIST database can be run by MATLAB, learning neural network.)
dropout_and_minibatch
- 基于两层BP神经网络，加入dropout和softmax，输出层使用softmax，实现对手写字符库MNIST的识别，正确率达90%。(Based on the two level BP neural network, adding dropout and softmax, the output layer uses softmax to realize the recognition of handwritten character library MNIST, the accuracy ra