March 2026
Intermediate
504 pages
11h 26m
Korean
Content preview from 파이토치로 배우는 LLM & AI
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201
7
장
자연어 처리를 위한 순환 신경망
자연어 처리를 위한 순환 신경망
CHAPTER
7
5
장에서 텍스트를 토큰화하고 시퀀스로 변환해 신경망에 주입할 수 있도록 문장을 숫자 텐서
로 만드는 방법을 살펴봤습니다.
6
장에서는 이를 확장해 감성 계산을 위해 비슷한 단어를 클러
스팅하는 임베딩을 알아보았습니다. 이 방법은 빈정거림 감지기를 구축하면서 보았듯이 매우
잘 작동합니다. 하지만 문장은 단순히 단어의 모음으로 구성된 것은 아니기 때문에 이런 방법
에는 한계가 존재합니다. 종종 단어의 등장 순서가 전체적인 의미를 결정합니다. 형용사는 나
란히 등장하는 명사의 의미를 추가하거나 바꿀 수 있습니다. 예를 들어 ‘
blue
’와 ‘
sky
’는 감성
측면에서는 의미가 없을 수 있지만 두 단어를 합친 ‘
blue
sky
(푸른 하늘)’는 명확하게 긍정적
인 감성을 가집니다. 다른 명사를 수식하는 명사도 있습니다. 예를 들면 ‘
rain
cloud
(비구름 )’,
‘
writing
desk
(글쓰기 책상)’, ‘
coffee
mug
(커피잔 )’와 같습니다.
이런 시퀀스를 고려하려면 부가적인 접근 방법이 필요합니다. 즉 모델 구조에
순환
recurrence
을 추가하는 것입니다. 이 장에서는 순환을 추가하는 여러 가지 방법을 알아보겠습니다. 시퀀
스 정보를 학습하는 방법과 이 정보를 사용해 ...
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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.