March 2026
Intermediate
504 pages
11h 26m
Korean
Content preview from 파이토치로 배우는 LLM & AI
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245
8
장
머신러닝으로 텍스트 생성하기
적절할 확률이 낮습니다. 이 단어를 시퀀스에 추가하고 다음 단어를 예측하면 올바른 확률이 낮
아질 가능성이 더 높아집니다. 따라서 예측된 단어가 거의 무작위한 것처럼 보이게 됩니다.
예를 들어 ‘
sweet
Jeremy
saw
Dublin
’에 있는 단어가 모두 말뭉치에 있지만 이 순서대로 있
지는 않습니다. 모델이 첫 번째 예측을 수행했을 때 단어 ‘
his
’가 가장 가능성 있는 후보로 선택
되었고
78
%의 꽤 높은 확률을 가졌습니다.
초기 텍스트: sweet Jeremy saw Dublin
생성된 단어 1: his
단계 1에서 상위 5개 예측:
his: 0.7782
go: 0.1393
bellows,: 0.0605
accident: 0.0090
til: 0.0048
이 단어를 시드 텍스트에 추가해 ‘
sweet
Jeremy
saw
Dublin
then
’을 만들면 훈련 데이터
에 없는 또 다른 구절이 됩니다. 이 문장에서 예측을 수행하면 가장 높은 확률인
44
%로 단어
‘
right
’이 선택됩니다.
생성된 단어 2: right
단계 2에서 상위 5개 예측: predictions for step :
right: 0.7678
pipes,: 0.1376
creature: 0.0458
didnt: 0.0136
youll: 0.0113 ...
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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.