Kronecker product 的基本运算 结合律 \begin{equation} \mathrm{A} \otimes (\mathrm{B + C}) = \mathrm{A} \otimes \mathrm{B} + \mathrm{A}\otimes \mathrm{C} \end{equation} \begin{equation} (\mathrm{A} + \mathrm{B} ) \otimes \mathrm{C} = \mathrm{A} \otimes \mathrm…

目录 定义 Stack Operator Kronecker Product 性质 Stack Operator Kronecker Product 半线性 Whitcomb L. Notes on Kronecker Products. 定义 Stack Operator 对于任意的矩阵\(A \in \mathbb{R}^{m \times n}\), \[vec(A) := [A_{00}, A_{10}, \ldots, A_{m-1,n-1}]^T \in \mathbb{R}^{mn…

Computational Geometry The Geometry Center (UIUC) Computational Geometry Pages (UIUC) Geometry in Action (UIC) Geometric Resource (UFL) CAGD Applets (UKA) Voronoi/Delaunay Applet (CornellUniversity) Directory of Computational Geometry Software (Dr. N…

%foo% is the syntax for a binary operator. In base R: %in%: '"%in%" <- function(x, table) match(x, table, nomatch = 0) > 0' %/% and %% perform integer division and modular division respectively, and are described on the ?Arithmetic help pa…

激活函数Relu 最近几年卷积神经网络中,激活函数往往不选择sigmoid或tanh函数,而是选择relu函数.Relu函数的定义 $$f(x)= max(0,x)$$ Relu函数图像如下图所示: CNN示例 上图是一个CNN的示意图,一个卷积神经网络由若干卷积层.Pooling层.全连接层组成.你可以构建各种不同的卷积神经网络,它的常用架构模式为: INPUT -> [[CONV]*N -> POOL?]*M -> [FC]*K 也就是N个卷积层叠加,然后(可选)叠加一个Poolin…

http://mathesaurus.sourceforge.net/matlab-numpy.html Help MATLAB/Octave Python Description dochelp -i % browse with Info help() Browse help interactively help help or doc doc help Help on using help help plot help(plot) or ?plot Help for a function h…

在 YouTube 上找到了慕尼黑工业大学(Technische Universitaet München)计算机视觉组 Daniel Cremers 教授的 Multiple View Geometry 课程.容易理解,收获颇多,写下笔记以巩固所学. 课程的 YouTube 地址为:https://www.youtube.com/playlist?list=PLTBdjV_4f-EJn6udZ34tht9EVIW7lbeo4 .视频评论区可以找到课程所使用课件与练习题的下载地址. 课程第1章介…

Policy Gradient Algorithms 2019-10-02 17:37:47 This blog is from: https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html Abstract: In this post, we are going to look deep into policy gradient, why it works, and many new polic…

目录:Matrix Differential Calculus with Applications in Statistics and Econometrics,3rd_[Magnus2019] Title -16 Contents -14 Preface -6 Part One - Matrices 1 1 Basic properties of vectors and matrices 3 1.1 Introduction 3 1.2 Sets 3 1.3 Matrices: additio…

目录 Lecture 1: Introduction Lecture 2: Properties and Random Graph Degree Distribution Path Length Clustering Coefficient Connectivity Erdos-Renyi Random Graph Model Small-World Model Kronecker Graph Model 最近在看 Stanford 的 Machine Learning with Graphs.…