March 2026
Intermediate
504 pages
11h 26m
Korean
Content preview from 파이토치로 배우는 LLM & AI
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84
1
부
모델 구축
이 예제는 이미지의 많은 정보가 축소되기 때문에 이미지를
특성
feature
1
으로 압축하는 필터를
학습할 수 있다는 것을 보여 줍니다. 이런 특성을 레이블에 매핑할 수 있습니다. 이전에 입력을
출력에 매핑하기 위해 뉴런의 파라미터를 학습시켰습니다. 비슷한 방식으로 입력을 출력에 매
핑하는 최상의 필터를 학습할 수 있습니다.
다음으로 특성은 유지하되 이미지에 있는 정보량은 줄일 수 있는 방법인 풀링을 살펴봅니다.
3.2
풀링
풀링
pooling
은 이미지 안에 있는 콘텐츠의 의미를 보존하면서 이미지의 픽셀을 줄이는 과정입니
다. 그림으로 보면 이해하기 쉽습니다. [그림
3
-
4
]는
최대 풀링
max
pooling
과정을 보여 줍니다.
그림
3-4
최대 풀링
여기에서 흑백 이미지 왼쪽 위에 있는 주황색 상자를 생각해 보죠. 이 상자 안에 든 픽셀을 추
출하면
2
×
2
배열이 됩니다. 이런 식으로
16
개의 픽셀을 네 개의
2
×
2
배열로 작게 나눕니
다. 이 배열을 풀
pool
이라고 합니다.
그다음 각 그룹에서 최댓값을 선택하고 이를 새로운 이미지로 재조립합니다. 따라서 왼쪽에 있
1
옮긴이_ 입력 데이터의 속성(패션
MNIST
의 경우 각 픽셀 값)을 종종 특성이라고 부릅니다. 번역서에서는
feature
을 문맥에 맞게 ‘특징’
또는 ‘특성’으로
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.