March 2026
Intermediate
504 pages
11h 26m
Korean
Content preview from 파이토치로 배우는 LLM & AI
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123
4
장
파이토치 데이터셋
파이토치 데이터셋
CHAPTER
4
3
장까지 다양한 데이터를 사용해 모델을 훈련했습니다. 패션
MNIST
데이터셋은 편리하게도
파이토치
API
에 포함되어 있었습니다. 이미지 기반 데이터인 말-사람 데이터셋, 강아지-고양
이 데이터셋은
ZIP
파일로 다운받아서 압축을 풀어 전처리했습니다. 여러분은 이미 모델을 훈
련하는 데 사용할 데이터를 구하는 여러 방법을 경험했습니다.
그러나 공개되어 있는 여러 데이터셋을 사용하려면 모델 구조를 고려하기 전에 해당 분야에 특
화된 다양한 기술부터 배우는 게 우선입니다.
torch
.
utils
.
data
.
Dataset
에서 제공하는 도
구의 목적은 데이터셋을 사용하기 쉽게 제공하는 것으로, 데이터를 수집하고 파이토치에서 사
용할 수 있는
API
형태로 만드는 전처리 단계를 대신 처리해 줍니다.
2
장에서 파이토치가 패션
MNIST
데이터를 처리하는 방법을 배우면서 이를 살짝 맛보았습니
다. 다시 정리하면 패션
MNIST
데이터는 다음 코드로 얻을 수 있습니다.
train_dataset = datasets.FashionMNIST(root='./data', train=True,
download=True, transform=transform)
이 데이터셋의 경우 패션
MNIST
데이터셋을 포함하고 있는
datasets ...
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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.