March 2026
Intermediate
504 pages
11h 26m
Korean
Content preview from 파이토치로 배우는 LLM & AI
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231
8
장
머신러닝으로 텍스트 생성하기
바꾼 후 토큰의 임베딩을 기반으로 문장을 분류했습니다. 하지만 이런 생성 모델을 훈련하기
위해 필요한 데이터를 만들려면 시퀀스를 입력 시퀀스와 레이블로 변환해야 합니다. 입력 시퀀
스는 단어의 그룹이고 레이블은 문장 안에 있는 다음 단어입니다. 그리고 입력 시퀀스를 레이
블에 매핑하는 모델을 훈련합니다. 따라서 향후 예측은 입력 시퀀스와 비슷한 레이블을 선택할
수 있습니다.
8.1
시퀀스를 입력 시퀀스로 변환하기
텍스트를 예측하려면 입력 시퀀스 (특성 )와 연관된 레이블로 신경망을 훈련해야 합니다. 시퀀
스를 레이블로 매핑하는 것이 텍스트 예측의 핵심입니다. 이 경우에는 분류 작업에서처럼 명시
적으로 레이블을 할당하지 않습니다. 대신 문장을 나누어
n
개 단어 블록에 대해 문장에 나오는
다음 단어가 레이블이 됩니다.
예를 들어 말뭉치에 ‘
Today
has
a
beautiful
blue
sky
’ 같은 문장이 있다면 ‘
Today
has
a
beautiful
blue
’는 특성으로, ‘
sky
’는 레이블로 나눌 수 있습니다. 그런 다음 텍스트 ‘
Today
has
a
beautiful
blue
’에 대해 예측을 수행하면 ‘
sky
’가 될 가능성이 높습니다. 훈련 데이터에
있는 다른 문장인 ‘
Yesterday
had
a
beautiful
blue
sky
’가 앞과 동일한 방식으로 분할되고,
그 이후에 ‘
Tomorrow
will
have
a
beautiful
blue
’에 대한 예측을 수행하면 다음 단어가
‘
sky
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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.
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.