March 2026
Intermediate
504 pages
11h 26m
Korean
Content preview from 파이토치로 배우는 LLM & AI
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313
11
장
시퀀스 모델을 위한 합성곱 신경망과 순환 신경망
이 장에서 보았듯이 신경망으로 시계열 데이터를 예측하는 것은 어렵지만 하이퍼파라미터를
튜닝하면 모델과 예측 결과를 향상할 수 있습니다.
11.6
양방향
RNN
사용하기
시퀀스를 분류할 때 고려할 수 있는 다른 방법은 양방향
RNN
입니다. 미래의 값이 과거의 값에
영향을 미치는 구조가 처음에는 직관적이지 않을 수 있습니다. 하지만 시계열이 시간에 따라
반복되는 계절성을 가질 수 있다는 것을 떠올려보세요. 신경망으로 예측을 만드는 과정은 정교
한 패턴 매칭이 전부입니다. 데이터가 반복된다면 데이터 반복의 신호를 미래의 값에서 찾을
수 있습니다. 양방향 층을 사용하면 모델을 훈련하여 타임 스텝
t
에서 타임 스텝
t
+
x
까지의
패턴과 타임 스텝
t
+
x
에서 타임 스텝
t
까지의 패턴을 감지할 수 있습니다.
다행히 양방향
RNN
을 만드는 방법은 간단합니다. 예를 들어 이전 절에서 소개한
GRU
를 사용
해 보죠. 양방향
GRU
를 만들려면
bidirectional
매개변수를 추가하면 됩니다. 이렇게 하면
각 타임 스텝에서 원래 시퀀스 순서의 역순으로 두 번 훈련하게 됩니다. 이 결과는 다음 스텝을
진행하기 전에 합쳐집니다.
예를 들면 다음과 같습니다.
class BidirectionalGRUModel(nn
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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.