August 2021
Beginner to intermediate
720 pages
15h 32m
Korean
Content preview from fastai와 파이토치가 만나 꽃피운 딥러닝
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fastai 애플리케이션 계층 이해하기
Dataset
에 기반한
DataLoader
는
Dataset
에 없는 몇 가지 추가 기능을 제공합니다. 따라서
Datasets
의 생성, 검증으로 시작한 다음에
DataLoaders
를 살펴보는 편이 쉽습니다.
DataBlock
객체는 단계적으로 구축해야 합니다. 특히 주피터 노트북을 사용해서 데이터를 단
계적으로 검증하면 좋습니다. 주피터 노트북 같은 대화형 환경은 탄력적인 코딩을 지속하고 발
생할 수 있는 문제를 검사하는 데 굉장히 유용합니다. 또한 현재 작성 중인 코드 블록에서 문제
가 발생하면 즉시 알아챌 수 있어서 디버깅도 쉽습니다!
가장 간단한 작업부터 해보죠. 디폴트 파라미터로 구성된 데이터블록을 생성합니다.
dblock = DataBlock()
DataBlock
객체로
Datasets
객체를 생성할 수 있습니다. 이때 실제 데이터 소스를 명시해야
합니다. 여기서는 데이터프레임이 데이터 소스죠.
dsets = dblock.datasets(df)
이렇게 만든
Datasets
에는
train
과
valid
라는
Dataset
형 속성이 있습니다. 다음은
train
속성에 접근하여 학습용 데이터셋의 데이터를 색인하는 코드입니다.
>>> dsets.train[0]
(fname 008663.jpg
labels car person ...
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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.