-
multisim正弦波方波三角波的函数发生器|课程 毕业设计
产生正弦波、方波、三角波的方案有很多种，可以先产生正弦波，然后通过整形电路将其变换成方波，再由积分电路将方波变成三角波；还可以先产生三角波——方波，再将三角波变换成正弦波。本设计首先产生方波与三角波，然后将三角波变换成正弦波的电路设计方法
- 2020-12-02下载
- 积分：1
-
《智能门锁原理图》pdf
《智能门锁原理图》pdf
- 2019-09-24下载
- 积分：1
-
高分三号SAR图像
网上搜集的一些高分三号的SAR图像，分辨率一般，已经是ipg格式的
- 2020-12-08下载
- 积分：1
-
基于协同过滤算法的电影推荐系统
该文件使用了协同的过滤的算法达成了电影推荐系统，比较适合新手，代码整洁，注释清晰
- 2020-11-29下载
- 积分：1
-
电赛题目测量放大器设计（Proteus完美仿真）
电赛题目测量放大器设计（Proteus完美仿真）（基于C语言设计，完美仿真）
- 2021-05-06下载
- 积分：1
-
【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
-
QT与Echart交互生成图形化表格
Echart是一款强大的js表格库通过QT与js交互的功能，向Echart传递数据，并动态加载出酷炫的Echart的表格
- 2020-12-12下载
- 积分：1
-
matlab心电信号小波分解 去噪
matlab下birge-massart算法计算阈值 基于小波函数分解的心电信号去噪
- 2021-05-06下载
- 积分：1
-
文本分类之词频统计（分词、词干提取、去停用词、计算词频，有界面）
主要是读取文本，然后进行分词、词干提取、去停用词、计算词频，有界面，很实用
- 2020-12-06下载
- 积分：1
-
TB6612双电机驱动_AD原理图及PCB
【实例简介】TB6612双电机驱动板，AD原理图PCB，18mm正方形，有BOM表和数据手册。可以直接打版。TB6612FNG每通道输出最高1 A的连续驱动电流，启动峰值电流达2A / 3A(连续脉冲/单脉冲);4种电机控制模式:正转/反转/制动/停止; PWM支持频率高达100 kHz;待机状态;片内低压检测电路与热停机保护电路;工作温度:-20~ 85°C; SSOP24小型贴片封装。
- 2021-11-18 00:50:46下载
- 积分：1
