资源列表
Autoencoder_Code
- G.E. hinton个人主页上的源代码，是关于06年那篇发表在science上的文章，了解的可以下载一下-GE hinton personal home page on the source code, is on the 2006 thing I read the article published in the science, to understand what can be downloaded
electromagnetic_field_numerical
- 一本介绍电磁场数值分析的书，分别介绍了有限差分，有限元，矩量法和最优化方法-1 Introduction Numerical Analysis of Electromagnetic Field book, introduced the finite difference, finite element, method of moments and optimization methods
lenet_test
- 包含mnist数据集的lenet例子,快速训练部分数据,达到85%的准确率(A lenet example that contains the MNIST dataset to quickly train part of the data to reach a 85% accuracy rate)
2_tf_mnist
- tensorflow，简单神经网络识别手写字符(Recognition of handwritten characters by neural network)
PCA+mnist
- 基于python，利用主成分分析（PCA）和K近邻算法（KNN）在MNIST手写数据集上进行了分类。 经过PCA降维，最终的KNN在100维的特征空间实现了超过97%的分类精度。(Based on python, it uses principal component analysis (PCA) and K nearest neighbor algorithm (KNN) to classify on the MNIST handwritten data set. After PCA dime
MNIST数据集
- 手写数字识别数据集的训练集和测试集，关于BP神经网络(Handwritten digit recognition data set)
zhenxishulvbo
- 整系数滤波器相关文献资料及整系数滤波器程序。-Integer coefficients filter literature data and the overall coefficient of the filter program.
CNN_mnist
- 使用CNN网络对mnist数据集进行训练(Use the CNN network to train MNIST data sets)
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
TheFiniteElementMethod
- The Finite Element Method Using MATLAB
ALEXNET
- 搭建的一个AlexNet的网络结构，包含原始AlexNet的最基本结构，使用的数据集为mnist，所以对其中的参数做出较小的修改，搭建网络方式比较典型，可根据范例结构自行扩展（包括原始mnist数据）(Build an AlexNet network structure, including the most basic structure of the original AlexNet, using a data set of mnist, so the parameters of the s
tensorflow_CNN
- 使用tensorflow实现CNN 模型(implement CNN model using tensorflow)