August 2021
Beginner to intermediate
720 pages
15h 32m
Korean
Content preview from fastai와 파이토치가 만나 꽃피운 딥러닝
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324
2
fastai 애플리케이션 계층 이해하기
7.6
분류 문제에 대한 손실의 이론적 표현에서, 타깃은 원-핫 인코딩됩니다 (실제로는 메모리를 절
약하려고 원-핫 인코딩을 피하는 경향이 있지만 마치 원-핫 인코딩을 사용한 것처럼 손실이
계산됩니다 ). 즉 모델은 단 하나에서만
1
을 반환하고 다른 모든 범주에 대해
0
을 반환하도록
학습되었습니다.
0
.
999
조차도 ‘충분하지’ 않습니다. 모델은 그레이디언트를 얻고 더 높은 신뢰
도로 활성을 예측하는 방법을 학습해나가죠. 그러나 이 과정을 계속하면 과적합이 조장되며 추
론 시 유의미한 확률을 제공하지 않는 모델이 만들어집니다. 완벽하게 예측된 범주를 확신하지
못하더라도, 그렇게 학습되었기 때문에 항상
1
이라고 예측합니다.
데이터를 완벽하게 레이블링하지 못하면 매우 해롭습니다.
2
장의 곰 분류 모델에서 일부 이미
지의 레이블을 잘못 지정했거나 두 종류의 곰이 한 이미지에 포함된 사례를 본 적이 있습니다.
일반적으로 데이터가 완벽할 수는 없습니다. 사람이 수작업으로 레이블링한다고 해도, 레이블
링이 어려운 이미지에서는 실수하거나 사람마다 의견이 갈릴 수 있습니다.
대신 모든
1
을
1
보다 약간 작은 수로 바꾸고
0
을
0
보다 약간 큰 수로 바꾼 다음 학습해볼 수 있
습니다. 이것이
기법입니다. ...
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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.