斯坦福大学@Coursera授权的机器学习课程中学习的各种机器学习（ML）算法的实现
Implementation of various Machine Learning (ML) Algorithms learned in the Machine Learning course authorised by Stanford University @ Coursera (2020-05-30, matlab, 0KB, 下载0次)
该知识库包含一些机器学习项目，作为斯坦福大学Andrew Ng教授在Coursera上的机器学习课程的实践。
This repository contains some machine learning projects as a practise on machine learning course on Coursera for Prof. Andrew Ng from Stanford University. (2020-05-07, matlab, 0KB, 下载0次)
Deep_Learning_Poker_Player_Using_MATLAB_andRaspberry_Pi，此软件包包括MATLAB脚本，可帮助您使用MATLAB、深度学习和Raspberry P设计扑克玩家...
This package includes MATLAB scripts that help you design a poker player using MATLAB, Deep Learning, and Raspberry Pi. The poker-playing algorithm consists of a deep learning network that predicts the cards, and a custom MATLAB algorithm that identifies ranked hands from the predictions and then makes bets like an actual player would. The (2020-05-07, matlab, 310KB, 下载0次)
机器学习-课程-斯坦福德大学，本报告面向任何需要帮助理解和解决吴恩达（Andrew Ng）的机器学习课程A...
This repo is for anyone who wants help in understanding and solving Andrew Ng s Coursera Course on Machine Learning Assignments and Quizes. (YEAR 2020) (2020-05-19, matlab, 29042KB, 下载0次)
MachineLearning,机器学习师从Andrew Ng(吴恩达),获得在Coursera平台上斯坦福大学Andrew Ng(吴恩达教授)机器学习(Machine Learning)的资格证书,为了有一个平台和大家分享和交流机器学习,因此特地在此进行课程的:...
MachineLearning, a machine learning teacher from Andrew Y. Ng, has obtained the qualification certificate of Stanford University s Andrew Ng (Professor Andrew Y. Ng) machine learning on the Coursera platform. In order to have a platform to share and exchange machine learning with everyone, the course is specially held here: (2019-10-15, matlab, 607785KB, 下载0次)
coursera-ml，该知识库包含斯坦福大学吴恩达（Andrew Ng）机器学习课程的所有课堂练习...
??This repository contains all of the lecture exercises of Machine Learning course by Andrew Ng, Stanford University @ Coursera. All are implemented by myself and in MATLAB/Octave. (2019-03-11, matlab, 48111KB, 下载0次)
Andrew Ng在史坦福大学深度学习教程的练习的答案，教程网址http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial。由于文件大小限制，所需数据已经删除，请自行到该教程网站下载。
Answer to Andrew Ng's practice in the Stanford University in-depth study course at http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial. Because the size of the file is limited, the required data has been deleted. Please download it yourself. (2018-10-10, matlab, 595KB, 下载3次)
某机构对20所高校的科研能力进行了调研和评价，试根据调研结果中较为重要的11个评价指标的数据，并结合离散Hopfield神经网络的联想记忆能力，建立离散Hopfield高校科研能力评价模型。
A mechanism for 20 universities scientific research ability of research and uation, test according to the research results of 11 important uation index data, and combined with discrete Hopfield neural network associative memory ability, discrete Hopfield college scientific research ability uation model is established. (2017-03-05, matlab, 81KB, 下载8次)
首先声明，这是别人写的LDA主题模型代码，本人测试过，可以运行，但是输出跟输出有点不尽人意，输入的是词的序号和该词在文档中出现的次数，要是可以直接读取文档就完美了。输出是主题以及词在该主题出现的概率，其中得到的主题我就看不懂了，不知道是算法问题，还是因为我的水平有限。在研究LDA主题模型的朋友，可以下载试一下
First statement, which is written by someone else LDA topic model code, I tested, you can run, but the output with the output bit unsatisfactory, enter the number of times the word number and word appears in a document, if you can read directly document is perfect. The output is the probability of topics and words in the topic appears, the theme of which was I would not read, do not know algorithmic problems, or because of my limited level. In the research LDA topic model friends, you can download a try (2016-06-07, matlab, 12KB, 下载8次)
将整个数据集中的103个样本随机划分为训练集与测试集，其中训练集包含80个样本，测
试集包含23个样本；
建立极限学习机模型，并训练；
利用训练好的极限学习机模型对测试集中的23个样本进行预测；
输出结果并绘图（真实值与预测值对比图）；
The 103 random samples of the entire data set is divided into training set and
test set, wherein the training set contains 80 samples, measuring
Test set contains 23 samples
Establish ELM model, and training
Limit the use of the trained learning machine model test set of 23 samples to
predict
Output and graphics (real and predicted values comparison chart)
(2016-03-25, matlab, 6KB, 下载127次)
使用支持向量机进行非线性回归，得到非线性函数y=f(x1,x2,…,xn)的支持向量解析式，
求解二次规划时调用了优化工具箱的quadprog函数。本函数在程序入口处对数据进行了
[-1,1]的归一化处理，所以计算得到的回归解析式的系数是针对归一化数据的，仿真测
试需使用与本函数配套的Regression函数。
Using non-linear support vector machine regression, nonlinear function y = f (x1, x2, ..., xn) support vector analytic, Optimization Toolbox quadprog call function when solving quadratic programming. The function of the data in the program carried out at the entrance [-1,1] of normalization, so the regression coefficients calculated analytical formula is for the normalization of data, simulation test test need to use this function supporting The Regression functions. (2014-05-17, matlab, 2KB, 下载18次)
斯坦福深度学习教程中关于stacked autoencoder的练习代码，源代码中需要补全的地方，全部把代码补完整，把手写体识别的数据库放到路径下，可以直接运行
Stanford deep learning tutorial exercises on stacked autoencoder code, source code need to fill all places, all the full complement of the code, the handwriting recognition into the path of a database, you can run directly (2013-06-21, matlab, 90KB, 下载231次)
斯坦福深度学习教程中关于Self-Taught的练习代码，源代码中需要补全的地方，全部把代码补完整，把手写体识别的数据库放到路径下，可以直接运行
Stanford deep learning tutorial exercises on Self-Taught code, source code need to fill all places, all the full complement of the code, the handwriting recognition into the path of a database, you can run directly (2013-06-21, matlab, 83KB, 下载65次)
斯坦福深度学习教程中关于softmax regression的练习代码，源代码中需要补全的地方，全部把代码补完整，把手写体识别的数据库放到路径下，可以直接运行
Stanford deep learning tutorial exercises on softmax regression code, source code need to fill all places, all the full complement of the code, the handwriting recognition into the path of a database, you can run directly (2013-06-21, matlab, 78KB, 下载359次)
网址：http://deeplearning.stanford.edu/wiki/index.php/Exercise:Sparse_Autoencoder斯坦福深度学习的教程，这个是稀疏编码的的练习，可以直接运行
URL: http://deeplearning.stanford.edu/wiki/index.php/Exercise:Sparse_Autoencoder Stanford deep learning tutorial, this is a sparse coding exercises that can be run directly (2013-06-21, matlab, 134KB, 下载417次)
降水短期气候预测是一个非常复杂、重要的研究课题。为了提高其预测能力，拟采用1959—2011 年逐月74 项大气环流特征量序列、月平均500 hPa 高度场和月平均海温场，选取预测因子；用主分量分析方法提取样本数据中主要信息为综合因子。用粒子群优化人工神经网络方法，建立宣城市夏季降水短期气候预测模型。对2007—2011 年宣城市夏季降水预报检验结果表明，粒子群优化人工神经网络收敛速度快，迭代次数少；试报平均绝对误差是66.5 mm，绝对值平均相对误差10.5 ，预测精度高，具有很好的应用推广前景。
Precipitation of short-term climate prediction is a very complex and important research topic. Intends to adopt in order to improve its ability to predict the the 1959-2011 monthly 74 atmospheric circulation feature series, monthly mean 500 hPa height field and monthly average sea surface temperature field, select the predictor extract the sample data using principal component analysis for the Synthesis factor. Artificial neural network using particle swarm optimization method, Xuancheng City in summer rainfall in short-term climate prediction model. 2007-2011 declared the city in summer precipitation forecast verification results show that the particle swarm optimization artificial neural network convergence speed, fewer iterations trial reported an average absolute error is 66.5 mm, the absolute value of the average relative error of 10.5 , high prediction accuracy, good application prospect. (2013-04-17, matlab, 1557KB, 下载46次)
小生境遗传算法Matlab工具箱试版,Niched Genetic Alogrihtm Matlab Toolbox - 接口文件说明:MyFun1.m - 一维优化函数,MyFun2.m - 二维优化函数, MyFun3.m - 三维优化函数,Main_GA1.m - 一维函数优化 - 主程序,Main_GA2.m - 二维函数优化 - 主程序, Main_GA3.m - 三维函数优化 - 主程序
Niche genetic algorithm Matlab toolbox trial version, Niched Genetic Alogrihtm Matlab Toolbox- Interface File Description: MyFun1.m- one-dimensional optimization function, MyFun2.m- two-dimensional optimization function, MyFun3.m- three-dimensional optimization function, Main_GA1.m- one-dimensional function optimization- the main program, Main_GA2.m- two-dimensional function optimization- the main program, Main_GA3.m- three-dimensional function optimization- the main program (2011-07-03, matlab, 72KB, 下载28次)
某小型书会有100本书，并排放在一个架子上，供大家借阅。大家每次借阅一本，看完后即放在书架的最左边。每一本书根据热门程度的不同赋予不同的值，该值介于0~1之间，且0表示最冷门，1表示最热门。假设每天每人只能借1本书，且需要当天归还。会所共有100人，每天借书的人服从参数为0.1的几何分布。试模拟该过程，并指出1年（365天）后书的排列情况，并探讨该排列与书热门程度之间的关系。提示：由于是随机模拟，故整个实验需要多次执行，并显示统计结果。
A small book of 100 books, and emissions in a shelf for everyone to borrow. Every time we borrow one, after reading on the bookshelf to the left. Each book is based on popularity of different given different value between 0 and 1, and 0 is the most popular, one that the most popular. By assuming only one book per person per day, and need to return the same day. Club 100 people a day, borrowing obey the geometric distribution of parameter 0.1. Try to simulate the process, and that one year (365 days) after the arrangement of the book, and to explore the arrangement and the relationship between the degree of popular books. Tip: Due to the stochastic simulation, so the need to repeatedly perform the experiment, and show results. (2011-06-27, matlab, 1KB, 下载6次)