• ## Extended tecdat|r language linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and canonical discriminant analysis (RDA)

Time：2021-12-22

Original link:http://tecdat.cn/?p=5689 Original source:Tuo end data tribal official account Discriminant analysis includes methods that can be used for classification and dimensionality reduction. Linear discriminant analysis (LDA) is particularly popular because it is both a classifier and a dimensionality reduction technique. Quadratic discriminant analysis (QDA) is a variant of LDA that allows nonlinear separation of data. […]

• ## [system architecture] understand the shengteng Da Vinci architecture computing unit

Time：2021-12-21

Welcome to my official account, to get more notes.   O_o   >_<   o_O   O_o   ~_~   o_O This paper explains the architecture and computing principle of computing unit in shengteng Da Vinci architecture in detail. 1. Da Vinci Architecture Overview Da Vinci architecture is a “domain specific architecture” (DSA) chip. The […]

• ## How to carry out variable screening and feature selection (II)? Optimal subset regression

Time：2021-12-20

01 model introduction Optimal subset regression is a kind of method for selecting independent variables of multiple linear regression equations. The best one is selected from the subset regression equation of all possible combinations of independent variables. For example, m independent variables will fit the 2m-1 subset regression equation, and then use the statistics of […]

• ## 06 equality constrained optimization algorithm

Time：2021-12-18

06 equality constrained optimization algorithm catalogue 1、 Introduction 2、 Equality constrained convex quadratic programming 3、 Newton method with equality constraints 4、 Solving KKT system 5、 Summary Convex optimization from getting started to giving up the complete tutorial address:https://www.cnblogs.com/nickchen121/p/14900036.html 1、 Introduction Note: Here we introduce the KKT condition to the equality constraint problem In the previous […]

• ## [NLP learns its 3.5] word embedding feature. Why are words related?

Time：2021-12-12

Characteristics of word embedding Now that you have a bunch of embedded vectors, we can start learning the characteristics between themPreviously on:https://www.cnblogs.com/DAYceng/p/14962528.html First rename the vectors to distinguish them Man corresponds to E_ man① Woman corresponds to E_ woman② King corresponds to E_ king③ Queen corresponds to E_ queen④ Now use E_ Man minus e_ […]

• ## G2o and global registration optimization of multi view point cloud

Time：2021-12-12

background I have a pile of 3D scattered point clouds after initial registration. After rough matching, all point clouds are basically unified in the same coordinate system, as shown in Figure 1 and Figure 2. In order to obtain better global results, it is necessary to optimize the global registration of the roughly matched point […]

• ## Common coordinate system and coordinate transformation of cesium (tools)

Time：2021-12-10

Common coordinate systems in ceisum include 1. Screen coordinates 2. World coordinates (Cartesian Cartesian coordinates) 3. Longitude and latitude can be in radian form and degree form 4. Webmercator web Mercator 5. Euler angle 6. Quaternion Look at a picture I drew myself and sort out the relationship between them The code GitHub address of […]

• ## [image algorithm] – how does the convolution kernel in convolution neural network (CNN) extract image features (Python realizes image convolution operation)

Time：2021-12-9

1. Preface We know that convolution kernel (also known as filter matrix) plays a very important role in convolution neural network. To put it bluntly, CNN is mainly used to extract various feature maps of images CNN mainly completes feature extraction through convolution operation. The image convolution operation mainly realizes the convolution operation by setting […]

• ## Graph neural network Chapter 5 graph signal processing and graph convolution neural network reading notes

Time：2021-12-6

A few words before: Finally! Here comes the most relevant content of GNN! The first four chapters are all preliminary knowledge, or introductory knowledge. In fact, they are not particularly related to GNN. But from the beginning of this chapter, it is the core of GNN: graph signal processing. This part is actually very critical, […]

• ## Matrix digital rain

Time：2021-12-6

yesterdayfakefishTaught meHow to use canvas to realize the digital rain of the matrix, that’s cool. I don’t understand JavaScript, so I translated it with coffeescript, so it looks much easier to understand: c = document.getElementById(‘c’) ctx = c.getContext(‘2d’) # full screen c.height = window.innerHeight c.width = window.innerWidth letters = “ɒdɔbɘʇǫʜiႱʞlmnoqpɿƨƚƚuvwxyzAᙠƆᗡƎᖷᎮHIᐴႱᐴ⅃MИOꟼỌЯƧTUVWXYƸ1234567890アイウエオカキクケコサシスセソタチツテトナニヌネノハヒフヘホマミムメモヤユヨラリルレロワヰヱヲンッ”.split(“”) font_size = 10 columns = […]

• ## Pandas from introduction to mastery (1) – Basics

Time：2021-12-5

We all know that Python can occupy a place in the field of data science, mainly due to the three swordsmen of data analysis: numpy, pandas and Matplotlib. Among the three libraries, I think pandas is the core and most used. Whether dealing with data or playing games, it is required to be able to […]

• ## Practical combat of neural network GCN code

Time：2021-12-4

GCN code practice The GCN code in section 5.6 of the book does the classification on the most classic Cora data set. The appropriate and inappropriate analogy of Cora to GNN is equivalent to MNIST to machine learning. I won’t repeat the introduction of Cora after searching a lot on the Internet. Here is the […]