September 2024
Beginner to intermediate
364 pages
8h 27m
Korean
Content preview from 머신러닝 인터뷰 실무 가이드
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149
CHAPTER 03
기술 인터뷰: 머신러닝 알고리즘
≈
.88 –1.08 0.9 1.09 –0.8
–.2 –.8 –1 .9 1
.9 –1 1 1 –.9
–0.9 1.0 –1.0 –1.0 0.9
0.38 0.6 1.2 –0.7 –1.18
–.11 –0.9
1 .1
–1 0
.2 –1
.1 1 –0.9 1.0 0.91
해리포터
벨빌의
세쌍둥이
다크나이트
라이즈
슈렉 메멘토
그림
3-12
행렬 분해
특이값 분해
singular
value
decomposition
(
SVD
)는 협업 필터링에 사용되는 고전적인 알고리즘입니
다. 그러나 높은 계산 비용 때문에, 저는 연습용 데이터 세트 이외에서
SVD
가 사용된 것을
본 적이 없습니다.
46
머신러닝 실무자로서 업계 애플리케이션에서 행렬 분해를 사용하기로
결정했다면, 많은 온라인 플랫폼에서 대량의 제품과 사용자를 다뤄야 하므로 선택한 알고리
즘은 매우 희소하고 큰 행렬을 처리할 수 있어야 합니다.
ALS
와 같은 알고리즘은 이러한 문
제에 대한 해결책을 제공합니다. 이 알고리즘들은 ‘선형 회귀 요약’에서 설명된 바와 같이, 전
통적인
SVD
가 사용하는 복잡한 행렬 조작 대신 최소 제곱법을 사용한 근사로 계산하는 방식
을 채택합니다.
추천 시스템에 관한 인터뷰 문제 예제
추천 시스템의 기본적인 내용을 다뤘으니 이제 몇 가지 인터뷰 문제 예제를 살펴보겠습니다. ...
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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.