May 2018
Beginner to intermediate
672 pages
16h 54m
Korean
Content preview from 핸즈온 머신러닝: 사이킷런과 텐서플로를 활용한 머신러닝, 딥러닝 실무
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292
1
부
머신러닝
8.7
연습문제
1
.
데이터셋의 차원을 축소하는 주요 목적은 무엇인가요? 대표적인 단점은 무엇인가요?
2
.
차원의 저주란 무엇인가요?
3
.
데이터셋의 차원을 축소시키고 나서 이 작업을 원복할 수 있나요? 할 수 있다면 어떻게 가
능할까요? 가능하지 않다면 왜일까요?
4
.
매우 비선형적인 데이터셋의 차원을 축소하는 데
PCA
를 사용할 수 있을까요?
5
.
설명된 분산을
95
%로 지정한
PCA
를
1
,
000
개의 차원을 가진 데이터셋에 적용한다고 가
정하겠습니다. 결과 데이터셋의 차원은 얼마나 될까요?
6
.
기본
PCA
, 점진적
PCA
, 랜덤
PCA
, 커널
PCA
는 어느 경우에 사용될까요?
7
.
어떤 데이터셋에 적용한 차원 축소 알고리즘의 성능을 어떻게 평가할 수 있을까요?
8
.
두 개의 차원 축소 알고리즘을 연결할 수 있을까요?
9
.
(
3
장에서 소개한)
MNIST
데이터셋을 로드하고 훈련 세트와 테스트 세트로 분할합니다
(처음
60
,
000
개는 훈련을 위한 샘플이고 나머지
10
,
000
개는 테스트용입니다). 이 데이
터셋에 랜덤 포레스트 분류기를 훈련시키고 얼마나 오래 걸리는지 시간을 잰 다음, 테스트
세트로 만들어진 모델을 평가합니다. 그런 다음
PCA
를 사용해 설명된 분산이
95
%가 되도
록 차원을 축소합니다. 이 축소된 데이터셋에 새로운 랜덤 포레스트 분류기를 훈련시키고
얼마나 오래 걸리는지 확인합니다. 훈련 속도가 더 빨라졌나요? 이제 테스트 세트에서 이
분류기를 평가해보세요.
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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.