March 2023
Beginner to intermediate
436 pages
9h 20m
Korean
Content preview from 머신러닝 시스템 설계
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257
7
장
모델 배포와 예측 서비스
7.3.1 7.3.1
저차원 인수분해저차원 인수분해
저차원 인수분해
low
-
rank
factorization
의 핵심 아이디어는 고차원 텐서를 저차원 텐서로 대체하는
것입니다.
23
한 가지 유형은 소형 합성곱 필터
compact
convolutional
filter
로, 매개변수 개수를 줄이고
속도를 높이기 위해 과도하게 매개변수화된(매개변수가 너무 많은) 합성곱 필터를 소형 블록
으로 대체합니다.
예를 들어, 스퀴즈넷
SqueezeNet
은
3
×
3
합성곱을
1
×
1
합성곱으로 대체하는 등 여러 전략을 사
용해
50
배 적은 매개변수로 이미지넷에서 알렉스넷 수준의 정확도를 달성합니다.
24
마찬가지로 모바일넷
MobileNet
은 크기
K
×
K
×
C
인 표준 합성곱을 깊이별 합성곱
depthwise
convolution
과 점별 합성곱
pointwise
convolution
(
1
×
1
×
C
)으로 분해합니다. 이때
K
는 커널 크기이
고
C
는 채널 개수입니다. 이는 각각의 새로운 합성곱이 매개변수를
K
2
C
개가 아니라
K
2
+
C
개만 사용함을 의미합니다.
K
=
3
이면 매개변수 개수가
8
~
9
배 감소합니다 (그림
7
-
9
).
25
(
a
) 표준 합성곱 필터
(
b
) 깊이별 합성곱 필터
(
c
) 깊이별 분리 가능한 합성곱의 맥락에서 점별
합성곱인
1
×
1
합성곱 필터
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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.