December 2020
Beginner to intermediate
256 pages
5h 15m
Korean
Content preview from 안전한 인공지능 시스템을 위한 심층 신경망 강화
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101
5
장
적대적 입력의 원리
5.1
입력 공간
DNN
은 복잡한 입력을 출력으로 연결하는 학습된 함수입니다.
3
장에서
Fashion
-
MNIST
데
이터셋을 사용하는 간단한 이미지 분류기를 살펴보았습니다. 또한
4
장에서는 딥러닝을 좀 더
복잡한 이미지 인식, 오디오 분류, 음성 데이터를 텍스트로 변환하는 것과 같은 일에 어떻게 적
용할 수 있는지를 이론적으로 설명했습니다.
앞서 설명한 예제들에서는 복잡한 입력 데이터를 사용합니다.
4
장에서 설명한
ResNet50
분
류기는 이미지넷 데이터를
224
×
224
픽셀로 자릅니다. 따라서 각 이미지는 총
50
,
176
픽셀
로 구성됩니다. 각 픽셀의 색상은 빨강, 초록, 파랑 총
3
개 채널로 표현되기 때문에
50
,
176
×
3
(
150
,
528
)값으로 이미지를 표현합니다. 이때 각 픽셀이 가질 수 있는 값은
0
과
255
사이의
값입니다. 그렇다면 이미지 분류기는
256
150528
개라는 엄청난 양의 이미지를 제공합니다!
같은 방식으로 계산하면,
1
비교적 해상도가 낮은
1
.
3
메가픽셀 사진은
256
3932160
개의 그림 변형
이 가능합니다. 그나마 저해상도의 단색
Fashion
-
MNIST
분류기에는
256
784
개의 입력이 가
능합니다.
2
DNN
에 입력 가능한 모든 이미지를 계산하는 한 가지 방법은 각각의 이미지를 고차원 ‘입력 공
간’ 내의 ...
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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.