资源列表
遗传算法改进的神经网络程序
- 自己整理网上的遗传算法改进的神经网络，matlab实现，主要应用于数据函数逼近拟合，网上的一般无法运行。这个可以运行。(The neural network improved by the genetic algorithm on the Internet, matlab implementation, mainly applied to the data function approximation fitting, the network is generally unable to run
chapter01
- conditional entropy, entropy, joint entropy, mutual information, relative entropy basic features are implemented in matlab approach
八数码问题
- 八数码问题也称为九宫问题。在3×3的棋盘，摆有八个棋子，每个棋子上标有1至8的某一数字，不同棋子上标的数字不相同。棋盘上还有一个空格，与空格相邻的棋子可以移到空格中。(Eight digital problems)
中文信息处理发展报告cips2016
- 中文信息处理报告，综述性文章，有较强的学习意义。(The Chinese information processing report, the summary article, has the strong learning significance.)
EX7_Trace
- 机器人基本运动 速度方向，速度模式的控制(The direction of the basic motion of the robot and the control of the velocity mode)
dangdang
- 运用python正则表达式分析网页（图书信息）(Using regular expressions to analyze web pages (book information))
xgBoost_Intro
- python xgboost分类程序的例子，需安装xgboost库。(An example of the python xgboost taxonomy requires the installation of the xgboost library.)
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)
alexnet_test
- 因上传文件大小的限制,仅包含了cifar10部分数据集,将32*32*3扩展到227*227*3,然后完全使用alexnet,短时间训练,可达75%的准确率(Due to the limitation of uploaded file size, it contains only part of cifar10 data set, extends 32*32*3 to 227*227*3, and then uses alexnet completely, and training for a
LIFT-master
- 基于训练端对端的深度卷积神经网络学习局部不变性特征(Local invariant feature of learning images based on the training end to end convolution neural network)
gcForest-master
- 基于决策树构建深度森林模型实现较高特征表示能力相比深度卷积神经网络(Building deep forest model based on decision tree to achieve higher feature representation ability compared with deep convolution neural network)
caffe-cvprw15-master
- 基于深度学习框架构建用于快速图像检索的二值哈希编码(Construction of binary hash codes for fast image retrieval based on depth learning framework)