이미 로컬에서 많은 작업이 이루어진 프로젝트를 Git 원격 저장소와 연동하는 경우, 다음의 방법을 사용한다.
git init
git remote add origin [깃 원격 저장소 URL]
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[Unity] Unity를 관리자권한으로 켜기.
2022. 1. 27. 01:11
가끔 유니티로 작업하다보면 Unity Editor에서 관리자 권한으로 켜야할 때가 있다.
그럴때 Unity hub를 관리자권한으로 실행해봤자 Editor까지 권한이 이양되지 않거나 에러가 생기게 된다.
방법은 다음과 같다.
Windows 명령 프롬프트 사용
1. 명령 프롬프트를 관리자권한으로 켠다.
2. 명령 프롬프트에 다음을 입력하여 Unity 프로젝트를 시작한다.
-> "<PathToEditor>" -projectPath "<PathToProject>"
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비지도 학습
2022. 1. 6. 00:44
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.
With unsupervised learning there is no feedback based on the prediction results.
Example:
Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.
Non-clustering: The "Cocktail Party Algorithm", allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).
지도학습
2022. 1. 3. 01:00
Supervised Learning
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorized into "regression" and "classification" problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
Example 1:
Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
We could turn this example into a classification problem by instead making our output about whether the house "sells for more or less than the asking price." Here we are classifying the houses based on price into two discrete categories.
Example 2:
(a) Regression - Given a picture of a person, we have to predict their age on the basis of the given picture
(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.