无人驾驶入门--Autoware使用手册

可用于初级出门学习机器学习的资料，图片库，1000多张车牌正样本，2000多张负样本

【实例简介】哈希表设计。为班级30个人的姓名设计一个哈希表，假设姓名用汉语拼音表示。要求用除留取余法构造哈希函数，用线性探测再散列法处理冲突，平均查找长度的上限为2。

用 matlab 仿真A*路径规划算法，有子函数可修改。解压后运行a_star.m即可。生成迷宫中的最短的路径，以及路径搜索的过程。

backtrack33-bt4 backtrack3是一款便携的linux系统，简称bt3。它能够支持U盘启动，光盘启动，硬盘启动，其中内置大量的网络安全检测工具及黑客破解软件。而spoonwep就是一款强大的图形化破解wep无线网络密码的工具，这是它的成名工具。想要研究的朋友可以下载试试！

该系统主要是基于labview实现的简单系统，能够实现温度的随机采集、曲线分析、平均值、最大值读取、以及历史数据的读取和打印功能

包括landsat8数据的NDVI、NDWI计算，辐射定标，提取landsat8数据的水域面积。注意改为自己的数据，代码可以摘用，不会用不要喷

完整版，带目录，机器学习必备经典；大部头要用力啃。Machine learning A Probabilistic PerspectiveMachine LearningA Probabilistic PerspectiveKevin P. MurphyThe mit PressCambridge, MassachusettsLondon, Englando 2012 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronic or mechanicalmeans(including photocopying, recording, or information storage and retrieval)without permission inwriting from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set in the HEx programming language by the author. Printed and bound in the UnitedStates of AmLibrary of Congress Cataloging-in-Publication InformationMurphy, Kevin Png:a piobabilistctive/Kevin P. Murphyp. cm. -(Adaptive computation and machine learning series)Includes bibliographical references and indexisBn 978-0-262-01802-9 (hardcover: alk. paper1. Machine learning. 2. Probabilities. I. TitleQ325.5M872012006.31-dc232012004558109876This book is dedicated to alessandro, Michael and stefanoand to the memory of gerard Joseph murphyContentsPreactXXVII1 IntroductionMachine learning: what and why?1..1Types of machine learning1.2 Supervised learning1.2.1Classification 31.2.2 Regression 83 Unsupervised learning 91.3.11.3.2Discovering latent factors 111.3.3 Discovering graph structure 131.3.4 Matrix completion 141.4 Some basic concepts in machine learning 161.4.1Parametric vs non-parametric models 161.4.2 A simple non-parametric classifier: K-nearest neighbors 161.4.3 The curse of dimensionality 181.4.4 Parametric models for classification and regression 191.4.5Linear regression 191.4.6Logistic regression1.4.7 Overfitting 221.4.8Model selection1.4.9No free lunch theorem242 Probability2.1 Introduction 272.2 A brief review of probability theory 282. 2. 1 Discrete random variables 282. 2.2 Fundamental rules 282.2.3B292. 2. 4 Independence and conditional independence 302. 2. 5 Continuous random variable32CONTENTS2.2.6 Quantiles 332.2.7 Mean and variance 332.3 Some common discrete distributions 342.3.1The binomial and bernoulli distributions 342.3.2 The multinomial and multinoulli distributions 352. 3.3 The Poisson distribution 372.3.4 The empirical distribution 372.4 Some common continuous distributions 382.4.1 Gaussian (normal) distribution 382.4.2Dte pdf 392.4.3 The Laplace distribution 412.4.4 The gamma distribution 412.4.5 The beta distribution 422.4.6 Pareto distribution2.5 Joint probability distributions 442.5.1Covariance and correlation442.5.2 The multivariate gaussian2.5.3 Multivariate Student t distribution 462.5.4 Dirichlet distribution 472.6 Transformations of random variables 492. 6. 1 Linear transformations 492.6.2 General transformations 502.6.3 Central limit theorem 512.7 Monte Carlo approximation 522.7.1 Example: change of variables, the MC way 532.7.2 Example: estimating T by Monte Carlo integration2.7.3 Accuracy of Monte Carlo approximation 542.8 Information theory562.8.1Entropy2.8.2 KL dive572.8.3 Mutual information 593 Generative models for discrete data 653.1 Introducti653.2 Bayesian concept learning 653.2.1Likelihood673.2.2 Prior 673.2.3P683.2.4Postedictive distribution3.2.5 A more complex prior 723.3 The beta-binomial model 723.3.1 Likelihood 733.3.2Prior743.3.3 Poster3.3.4Posterior predictive distributionCONTENTS3.4 The Dirichlet-multinomial model 783. 4. 1 Likelihood 793.4.2 Prior 793.4.3 Posterior 793.4.4Posterior predictive813.5 Naive Bayes classifiers 823.5.1 Model fitting 833.5.2 Using the model for prediction 853.5.3 The log-sum-exp trick 803.5.4 Feature selection using mutual information 863.5.5 Classifying documents using bag of words 84 Gaussian models4.1 Introduction974.1.1Notation974. 1.2 Basics 974. 1.3 MlE for an mvn 994.1.4 Maximum entropy derivation of the gaussian 1014.2 Gaussian discriminant analysis 1014.2.1 Quadratic discriminant analysis(QDA) 1024.2.2 Linear discriminant analysis (LDA) 1034.2.3 Two-claSs LDA 1044.2.4 MLE for discriminant analysis 1064.2.5 Strategies for preventing overfitting 1064.2.6 Regularized LDA* 104.2.7 Diagonal LDA4.2.8 Nearest shrunken centroids classifier1094.3 Inference in jointly Gaussian distributions 1104.3.1Statement of the result 1114.3.2 Examples4.3.3 Information form 1154.3.4 Proof of the result 1164.4 Linear Gaussian systems 1194.4.1Statement of the result 1194.4.2 Examples 1204.4.3 Proof of the result1244.5 Digression: The Wishart distribution4.5. 1 Inverse Wishart distribution 1264.5.2 Visualizing the wishart distribution* 1274.6 Inferring the parameters of an MVn 1274.6.1 Posterior distribution of u 1284.6.2 Posterior distribution of e1284.6.3 Posterior distribution of u and 2* 1324.6.4 Sensor fusion with unknown precisions 138

【实例简介】用MFC与SQLServer写的商品销售系统，功能包括：设置店长（管理员）、店员；录入与修改数据库中的商品信息；按年月计算利润等。

这是使用MATLAB语言实现小波分析在图像压缩中应用的一个算法--"嵌入式小波零树编码算法（EZW）"的一个带有界面的小程序

本源码是基于PHP+Mysql实现的车辆管理系统（车管所），包括用户与管理员的角色，实现了完整的增删改查功能模块，结构简单，思路清晰，代码严谨但不复杂。运行环境是WAMPSERVER，开发工具使用的是Dreamweaver，适合用于PHP入门级学员的学习交流，或者简单课设的参考源码。