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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126
1
부
머신러닝
하지만 잠깐만요 ! 데이터를 자세히 조사하기 전에 항상 테스트 세트를 만들고 따로 떼어놓아야
합니다. 사실
MNIST
데이터셋은 이미 훈련 세트 (앞쪽
60
,
000
개 이미지 )와 테스트 세트(뒤
쪽
10
,
000
개 이미지 )로 나누어 놓았습니다.
X
_
train
,
X
_
test
,
y
_
train
,
y
_
test
=
X
[:
60000
],
X
[
60000
:],
y
[:
60000
],
y
[
60000
:]
훈련 세트를 섞어서 모든 교차 검증 폴드가 비슷해지도록 만들겠습니다(하나의 폴드라도 특정
숫자가 누락되면 안 됩니다 ). 더군다나 어떤 학습 알고리즘은 훈련 샘플의 순서에 민감해서 많
은 비슷한 샘플이 연이어 나타나면 성능이 나빠집니다. 데이터셋을 섞으면 이런 문제를 방지할
수 있습니다.
2
import numpy as np
shuffle
_
index
=
np
.
random
.
permutation
(
60000
)
X
_
train
,
y
_
train
=
X
_
train
[
shuffle
_
index
],
y
_
train
[
shuffle
_
index
]
3.2
이진 분류기 훈련
문제를 단순화해서 하나의 숫자, 예를 들면 숫자
5
만 식별해보겠습니다. 이 ‘
5
-
감지기’는 ‘
5
’와
‘
5
아님’ 두 개의 클래스를 구분할 수 있는 이진 분류기
binary
classifier
의 한 예입니다. 분류 작업을
위해 ...
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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.