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小迈步第一课： MATLAB深度学习入门课堂.pdf
【实例简介】2019.03.14 MATLAB公开教程的PPT，课程内容：
1. 简介
深度学习：直接从数据中进行学习
卷积神经网络：用于图像识别、目标检测、语义分割等任务
2. MATLAB的优势
支持与Tensorflow/Pytorch等开源框架协作
简单易学，高质量的帮助文档和大量示例
高效的开发平台，完整的工具链
实用的数据标注和可视化工具
强大的代码生成功能
支持多平台部署
3. 仅用11行代码实现图像分类
1行代码直接导入经典的Alexnet网络模型
通过摄像头实时采集图像数据，可识别1000种常见物体
4. 迁移学习的力量
基于已有的卷积神经网络进行微调，实现专属图像识别
丰富的预训练模型，包括SqueezeNet, ResNet-18, DenseNet-201, Inception-ResNet-v2等
5. 设计复杂网络的利器：Deep Network Designer
图形交互界面，便于设计和修改复杂的网络
支持一键分析，自动修正网络结构中的错误
6. MATLAB与Tensorflow/Pytorch等开源框架的协作
通过ONNX (Open Neural Network Exchange)共享深度学习模型
支持直接导入Keras和Caffe的模型
- 2021-11-23 00:31:43下载
- 积分：1
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基于双目视觉的深度计算和三维重建
基于双目视觉的深度计算和三维重建，代码我自己用过，绝对没问题
- 2020-12-03下载
- 积分：1
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蓝牙协议大全
很全的蓝牙协议，包含目前所有的核心协议（最新到5.0）以及常用profile协议。
- 2021-05-06下载
- 积分：1
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MMC变流器仿真模型
利用MATLAB搭建的MMC仿真模型 ，调试方式采用载波移相方式
- 2020-11-28下载
- 积分：1
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Excel决策树插件
Excel决策树插件Treeplan,Excel决策树插件Treeplan,Excel决策树插件Treeplan
- 2021-05-06下载
- 积分：1
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STM32F407开发板集成程序
本程序集成了 AD8370、AD9851、CDCE937、ADS1271的驱动程序；有FFT复数变换、FIR滤波、IIR滤波STemwin5.32机械按键状态机（支持短按、长按、持续按、组合按）PS2键盘（使用串口接收数据）
- 2020-12-01下载
- 积分：1
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Qt实现动态曲线图+文本数据的实时显示
提供的为完整源代码可运行。Qt实现文本实时显示，利用QFile读取在不断刷新的文本文件，并用QTextBrowser组件进行实时显示，以及动态曲线图的绘制。
- 2020-12-03下载
- 积分：1
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最大功率跟踪（扰动观察法和电导增量法）
扰动观察法：PO80025.mdl是温度保持25℃不变，0.1s时光照强度由1000W/m2瞬间下降到800W/m2的情况；PO100045.mdl是光照强度保持1000W/m2不变，0.1s时温度由25℃瞬间上升到45℃的情况；电导增量法：IC80025.mdl是温度保持25℃不变，0.1s时光照强度由1000W/m2瞬间下降到800W/m2的情况；IC100045.mdl是光照强度保持1000W/m2不变，0.1s时温度由25℃瞬间上升到45℃的情况.
- 2020-12-12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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SX1212官方驱动程序及说明（中英文对照）
超低接收电流芯片SX1212官方驱动程序及说明文档
- 2020-12-09下载
- 积分：1
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