September 2024
Beginner to intermediate
364 pages
8h 27m
Korean
Content preview from 머신러닝 인터뷰 실무 가이드
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147
CHAPTER 03
기술 인터뷰: 머신러닝 알고리즘
니다. 사람들은 제품을 매우 좋아하거나 싫어할 경우 명시적인 리뷰를 남길 가능성이 더 높
기 때문입니다(우리는 무언가 잘못되었을 때 사람들이 더 목소리를 높인다는 사실을 잘 알고
있습니다 ).
TIP
4
장에서는 모델 학습과 데이터 전처리에 대해 설명합니다. 추천 시스템 애플리케이션의 경우 탐색적 데
이터 분석
exploratory
data
analysis
, 모델 학습, 데이터 전처리, 피처 엔지니어링, 평가 및 모니터링 과정에서 사용
가능한 명시적 및 암시적 평가를 이해하는 것은 관련 인터뷰에서 중요한 논의 주제입니다.
콘텐츠 기반 추천 시스템 요약
콘텐츠 기반
content
-
based
추천 시스템은 또다른 대표적인 유형의 추천 시스템입니다. 콘텐츠 기
반 추천 시스템에서는 제품 자체에 대한 상세한 정보가 필요합니다. 이 정보는 텍스트 설명
(책 소개, 영화 장르 및 설명 ), 이미지 (제품 스크린샷 ), 오디오/영상 (트레일러, 제품 영상 )
등의 피처를 포함하여, 어떤 항목들이 서로 유사한지를 파악하는 데 사용됩니다. 반면에 앞
서 설명한 사용자 및 항목 기반 협업 필터링은 항목이나 제품에 대한 사용자의 선호도에 의존
하지만, 항목 자체의 피처에는 의존하지 않습니다.
예를 들어 콘텐츠 기반 영화 추천 시스템은 ...
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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.