March 2026
Intermediate
504 pages
11h 26m
Korean
Content preview from 파이토치로 배우는 LLM & AI
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115
3
장
고급 컴퓨터 비전: 이미지에서 특징 감지하기
3.8
다중 분류
지금까지 본 모든 예제는 두 옵션 중 하나를 선택하는
이진 분류
binary
classification
모델을 만들었습
니다. 다중 분류
multiclass
classification
모델도 거의 비슷하지만 몇 가지 차이점이 있습니다. 출력 층
에 시그모이드 활성화 함수를 사용하는 하나의 뉴런을 사용하는 것이 아니라, 분류하려는 클
래스 개수에 해당하는
n
개의 뉴런을 사용합니다. 또한 여러 개의 클래스에 적합한 손실 함수로
바꾸어야 합니다.
파이토치의
nn
.
CrossEntropyLoss
손실 함수는 여러 개의 클래스를 다룰 수 있습니다. 따라
서 이 장에서 만든 ‘강아지-고양이’와 ‘말-사람’ 전이 학습 분류기는 수정 없이 이 손실 함수를
사용할 수 있습니다. 하지만 단일 뉴런으로 만든 ‘말-사람’ 분류기는 두 개 이상의 클래스에 대
한 확률을 출력하지 않으므로 이 함수를 사용할 수 없습니다. 이 점을 항상 주의해야 하며 분류
모델을 만들 때 발생하는 흔한 버그 중 하나입니다.
이진 분류 다음으로 가위, 바위, 보 게임을 다루어 보죠. 손 모양을 인식하는 모델을 훈련하려면
세 가지 종류의 이미지를 다루어야 합니다. 다행히 이를 위해 간단한 데이터셋(
https
://
bit
.
ly
/
4aAeFv7 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.