资源列表
rbf500
- RBF神经网络建模，也可适用于多输入多输出(RBF neural network modeling can also be applied to multi input and multi output)
BPNN
- 采用BP神经网络设计男女生分类器。采用的特征包括身高、体重、是否喜欢数学、是否喜欢文学、是否喜欢运动共五个特征，BP神经网络包含一个隐层，隐层结点数为6。(BP neural network is used to design the classifier for boys and girls. The features used include height, weight, whether you like mathematics, whether you like literature,
cartographer--zhushi
- cartographer是一个系统，提供实时同步定位和测绘(SLAM)在二维和三维跨多个平台和传感器配置。(Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.)
垃圾短信分类
- 基于文本内容的垃圾短信识别,对数据进行了数据清洗,分词等,进行 了模型训练及评价(Based on the text content of spam short message recognition, data cleaning, segmentation, model training and evaluation are carried out)
人工神经网络
- 基于MATLAB的人工神经网络学习，内涵代码和案例数据(Artificial neural network learning based on MATLAB, connotation code and case data)
RBF
- 径向基函数神经网络，基本算例.........(radial basis function)
mnist_dataset_csv
- 已经转换过来的mnist数据集的csv格式(CSV format of the converted MNIST dataset)
深度（多层）极限学习机的python实现
- 深度极限学习机也叫多层极限学习机，ML-ELM。是黄广斌等人在极限学习机ELM基础上，将其拓展为深度学习的一种模式识别方法，原文文章：Representational learning with extreme learning machine for big data。(The deep extreme learning machine is also called the multi-layer extreme learning machine, ML-ELM. It is Huang Gu
心脏病诊断
- 对给定病人数据做心脏病诊断。数据集来自克利夫兰临床基金会，是美国最大的心脏外科中心。(Heart disease diagnosis was made for given patient data. The data set comes from the Cleveland Clinic Foundation, which is the largest heart surgery center in the United States.)
花的分类问题
- 神经网络是一组连接的输入/输出单元，其中每个连接都与一个权重相关联。在学习阶段，通过调整这些权重，能够预测输入元素的正确类标号(A neural network is a set of connected input/output units, where each connection is associated with a weight. In the learning phase, by adjusting these weights, the correct class label o
code
- 利用python编写室内定位机器学习，计算结果，室内定位(Indoor Localization by wifi)
李宏毅—1天搞懂深度学习
- 本文是2016 台湾资料科学年会前导课程“一天搞懂深度学习”的全部讲义PPT（共268页），由台湾大学电机工程学助理教授李宏毅主讲。作者在文中分四个部分对神经网络的原理、目前存在形态以及未来的发展进行了介绍。深度学习的每一个核心概念在文中都有相关案例进行呈现，通俗易懂。一天的时间搞懂深度学习？其实并不是没有可能。(This is the entire handout PPT (268 pages in total) of "a day to understand deep learni