March 2026
Intermediate
504 pages
11h 26m
Korean
Content preview from 파이토치로 배우는 LLM & AI
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269
10
장
시퀀스를 예측하는 머신러닝 모델 만들기
시퀀스를 예측하는
머신러닝 모델 만들기
CHAPTER
10
9
장에서 시퀀스 데이터와 트렌드, 계절성, 자기상관, 잡음 같은 시계열의 특징을 소개했습니
다. 합성된 시계열 데이터를 만들고 통계적인 방법으로 예측하는 방법을 알아보았습니다. 이어
지는 장에서는 머신러닝을 사용해 예측하는 방법을 살펴봅니다. 모델을 만들기 전에 먼저 머신
러닝 모델을 훈련하기 위해 시계열 데이터를 구성하는 방법을 이해해야 합니다. 소위 윈도 데
이터셋
window
dataset
이라는 것을 만듭니다.
윈도 데이터셋이 필요한 이유를 알아보기 위해
9
장에서 만든 시계열 데이터를 다시 떠올려봅
시다. 앞선 시계열 데이터는 [그림
10
-
1
]과 같았습니다.
값
시간
그림
10-1
합성 시계열 데이터
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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.