December 2020
Beginner to intermediate
256 pages
5h 15m
Korean
Content preview from 안전한 인공지능 시스템을 위한 심층 신경망 강화
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231
10
장
방어
습니다.
9
임계값은 다음 노트에 설명한 것처럼
ROC
곡선을 사용해 설정할 수 있습니다.
NOTE
_
ROC
곡선
머신러닝에서 중요한 도구는 ‘수신자 조작 특성
receiver
operating
characteristic
(
ROC
)’ 곡선입니다. 이 곡선은 이진 결
정을 내리는 예측 임계값을 설정하는 문제를 해결하기 위해 종종 사용합니다.
ROC
곡선은 ‘
ROC
공간’에서
거짓 양성 비율에 대한 참 양성의 비율을 표시합니다. 참 양성(이점)과 거짓 양성(비용)을 비교해 임계값을 설
정하는 데 이 곡선을 사용할 수 있습니다.
적대적 방어의 경우, 이진 임계값은 입력이 ‘적대적’일 가능성이 의미하는 임계값일 수 있습니다. 예를 들어
방어가
50
% 확실할 때 적대적 입력을 식별하면 실제 비율은 얼마가 될까요? 즉, 이 임계값으로 인해 적대적
입력으로 잘못 분류된 비적대적 입력이 몇 개나 될까요? 실제 결과가 아닌 것에 대한 허용 가능한 임계값은
상황에 따라 다릅니다. 비적대적 데이터의 정확도를 낮추는 대가로 적대적 입력을 받는 것이 더 중요할까요?
ROC
곡선을 사용해 이를 평가의 일부로 설명할 수 있으므로 특정 상황에 대한 임계값을 설정할 수 있습
니다.
주피터 노트북을 사용해 다른 공격이나 드롭아웃 모델 자체에서 생성한 적대적 데이터를 실험
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and much more.
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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.
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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.