August 2024
Beginner to intermediate
640 pages
14h 53m
Korean
Content preview from AI를 위한 필수 수학
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188
AI
를 위한 필수 수학
카이밍 히
Kaming
He
초기화
초기 가중치를 평균이
0
, 분산이
2
/
n
인 가우스 분포에서 샘플링한다. 여기서
n
은 노드의 입력
수이다.
4.5
정규화
정규화는 훈련 함수에 대해 적절한 가중치를 선택하고 데이터에 대해 과적합을 방지하는 데 도
움이 된다. 즉, 훈련된 함수가 노이즈에 강하고 본 적 없는 데이터에 대해서도 잘 일반화할 수
있게 한다. 이번에는 신경망을 훈련할 때 사용되는 간단하고 인기 있는 정규화 방법 네 가지를
살펴본다.
4.5.1
드롭아웃
드롭아웃
dropout
은 훈련 중에 각 층에서 무작위로 선택된 뉴런을 제거하는 것이다. 보통 입력 층
의 노드 중 약
20
%, 각 은닉 층의 노드 중 절반 정도를 무작위로 제거한다. 출력 층 노드는 제
거하지 않는다. 드롭아웃은 부모 유전자의 절반이 사라지고 돌연변이가 소량 발생하는 유전적
재생산에서 영감을 얻었다. 이는 각 층마다 노드 수가 다른 여러 신경망을 한 번에 훈련하고 그
결과를 평균하는 효과가 있으며, 일반적으로 더 신뢰할 수 있는 결과를 생성한다.
드롭아웃을 구현하는 한 가지 방법은 각 층마다 해당 층의 각 노드가 제거될 확률을 지정하는
하이퍼파라미터
p
를 추가하는 것이다. 이전 층 노드의 출력을 선형 조합한 다음 활성화시는
각 노드의 기본 연산을 기억하자. 드롭아웃을 ...
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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.