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Unity 3D NGUI实战教程(完整版)
Unity 3D NGUI实战教程 完整版第1章 初识NGUI1.1 游戏UI开发介绍1.1.1 什么是游戏UI1.1.2 UI为何如此重要1.1.3 UI开发的流程1.1.4 UI开发的难点1.2 什么是NGUI1.2.1 NGUI插件介绍1.2.2 NGUI的强大优势第2章 NGUI基础2.1 导入NGUI插件2.1.1 NGUI版本介绍2.1.2 NGUI的下载和购买2.1.3 导入NGUI插件应用2.1.4 导入常见问题2.2 认识基本的UI资源2.2.1 什么是UI精灵（Sprite）2.2.2 什么是UI图集（Atlas）2.2.3 什么是UI
- 2020-12-09下载
- 积分：1
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CMW WLAN非信令射频测试应用指南_V1.0
CMW Non-Signaling测试相关
- 2020-12-09下载
- 积分：1
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FANUC ROBOT PMC 编程软件
本软件用于FANUC ROBOT PMC编程，经过几年使用我感觉该软件非常不错，对FANUC ROBOT PMC感兴趣的朋友不妨试试。
- 2020-12-04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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《Hadoop大数据技术》课程设计报告.docx
Hadoop流量分析，用户上网流量统计
- 2021-05-06下载
- 积分：1
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旅游路线设计 数学建模
旅游路线设计 数学建模 对于观光旅游、文化考察，选择合理的旅游路线来达到省钱省事的最佳效果是首先要考虑的事情。本文通过对新疆的观光旅游和文化考察，根据各景区的最佳逗留时间、行车路线和旅游路线等特征，建立了新疆最佳旅游的图论模型和数学规划模型，提出了求解的近似算法，并运用MATLA求解，得到了最佳旅游路线的设计方案，为旅游者和文化考察者提供了理论依据和参考。
- 2020-12-02下载
- 积分：1
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ArcGIS Engine 10 Runtime 安装包 - Part 4 / 6
ArcGIS Engine 10 Runtime，是运行基于ArcGIS Engine 10开发的程序必须的库文件，通常我们把这个Runtime整合在安装包中。总共有6个压缩包，请依次下载。
- 2020-12-07下载
- 积分：1
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混合动力汽车能耗模型与能量管理动态规划matlab
【实例简介】分为三个文件，一个是能耗模型，一个是动态规划程序，一个是主程序。直接运行主程序即可。含注释
- 2021-11-04 00:32:43下载
- 积分：1
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高级专家系统：原理、设计及应用
仅供学习交流使用
- 2020-12-10下载
- 积分：1
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流形学习的matlab实现代码（mani.m）绝对好用
流行学习的matlab代码，包括各种流形学习方法:MDS/ISOMAP/LLE/HLLE/LE/LTSA…… 图形界面，操作简单。
- 2020-12-12下载
- 积分：1
