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java 五子棋游戏
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- 2020-11-29下载
- 积分：1
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Qt编写的网络五子棋
Qt写的网络五子棋代码，使用了QTcpSocket类，进行网络通信。
- 2021-05-07下载
- 积分：1
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基于SSH的招聘及人事管理系统的设计与实现（含源码+毕业论文）
含完整的源码及数据库脚本、毕业论文。本网站采用JAVA语言开发，主要采用Struts2、Hibernate、Spring三大框架技术，实现了一个基于web的B/S模式的人才招聘网。
- 2020-11-28下载
- 积分：1
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软件工程实验报告-万年历
输入一个年份（1-3000），然后显示12个月的月历，如屏幕显示不下，则应提供翻页功能。
- 2020-12-07下载
- 积分：1
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GAMMA软件安装教程以及安装所需要的各种包
GAMMA software update2011安装教程以及，各种包，你！懂！得！个人安装过不下5台，绝对有效。无效出门被车怼。GAMMA软件是瑞士公司出品的 sar 影像处理软件，地球最强没有之一。能D-inSAR，能生成dem，能sbas,能ps-insar。通过命令式操作轻松完成各种大型任务。。。
- 2020-12-06下载
- 积分：1
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【PDF】《Machine learning A Probabilistic Perspective》 MLAPP；by Kevin Murphy
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
- 2020-12-10下载
- 积分：1
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SerialPort_VS2019.rar
【实例简介】首先感谢原创版主“雪影藏刀”提供的“基于VS2019的串口调试工具开发”，在此基础上修正了串口16进制不能发送和接收“00”的问题，以及16进制接收大于等于0x80就输出一大串FFFF(补码)的问题; 用此源代码工程可快速开发串口上位机程序。
开发平台为：Visual Studio 2019社区版(vs community2019)
- 2021-11-16 00:43:18下载
- 积分：1
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北京昆仑通态组态软件MCGS高级教程
经过培训，您可以熟练应用MCGS组态软件中的各项功能；能根据工艺现场要求熟练制作动画流程；能处理相对复杂的曲线、报表、数据后处理等需求；能熟练使用MCGS脚本程序进行复杂的工业流程控制。
- 2020-07-03下载
- 积分：1
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免费漂亮的WPF界面框架
基于FirstFloor.ModernUI的免费WPF界面框架，演示了如何自定义界面字体实现。该框架界面简洁明快，易于二次开发，可开发出靓丽的应用软件。时间久远，自主降低积分
- 2020-12-06下载
- 积分：1
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python27_d.dll
python27_d.dll和python27_d.lib
- 2020-11-29下载
- 积分：1
