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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272
1
부
머신러닝
8.2
차원 축소를 위한 접근 방법
구체적인 차원 축소 알고리즘을 알아보기 전에 차원을 감소시키는 두 가지 주요한 접근법인 투
영
projection
과 매니폴드 학습
Manifold
Learning
을 살펴보겠습니다.
8.2.1
투영
대부분의 실전문제는 훈련 샘플이 모든 차원에 걸쳐 균일하게 퍼져 있지 않습니다. 많은 특성
은 거의 변화가 없는 반면, (앞서 말한
MNIST
의 경우처럼 ) 다른 특성들은 서로 강하게 연관되
어 있습니다.
8
결과적으로 모든 훈련 샘플이 사실 고차원 공간 안의 저차원 부분 공간
subspace
에
(또는 가까이) 놓여 있습니다.
9
너무 추상적인 말이라 예를 들어 살펴보겠습니다. [그림
8
-
2
]
에 원 모양을 띤
3
차원 데이터셋이 있습니다.
그림
8-2
2
차원에 가깝게 배치된
3
차원 데이터셋
8
옮긴이_
MNIST
이미지의 테두리는 거의 항상 흰색이고 중심부의 픽셀들은 인접 픽셀과 강하게 연관되었다는 점을 이 장의 서두에서 설
명했습니다.
9
옮긴이_ 거의 모두 흰색인 테두리 부분을 떼어놓고 생각하면 대부분의 이미지가 더 낮은 차원(적은 픽셀)인 부분 공간에 놓여 있다고 말
할 수 있습니다.
273
8
장
차원 축소
모든 훈련 샘플이 거의 평면 형태로 놓여 있습니다. 이것이 고차원(
3D
) 공간에 있는 저차원
(
2D
) 부분 공간입니다. ...
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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.