March 2026
Intermediate
504 pages
11h 26m
Korean
Content preview from 파이토치로 배우는 LLM & AI
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166
1
부
모델 구축
5.4
마치며
이전 장에서 이미지를 사용해 분류 모델을 만들었습니다. 이미지는 너비, 높이, 포맷을 알고 있
으므로 차원이 잘 정의된 데이터입니다. 반면 텍스트는 다루기 훨씬 어렵습니다. 구조적이지
않은 경우가 많고, 포맷팅 요소와 같은 불필요한 콘텐츠를 담고 있거나 원하는 내용이 항상 포
함되어 있지 않을 수 있습니다. 따라서 의미 없고 관련 없는 콘텐츠를 제거해야 하는 경우가 많
습니다.
이번 장에서 단어 토큰화를 통해 텍스트를 숫자로 바꾸는 방법과 다양한 포맷의 텍스트를 읽고
필터링하는 방법을 알아보았습니다. 다음 단계에서는 이런 기술을 바탕으로 단어에서 의미를
추출하는 방법을 배워보겠습니다. 미리 귀띔해주자면 첫 번째 단계는 자연어 이해입니다.
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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.