May 2022
Beginner to intermediate
448 pages
13h 7m
Korean
book
밑바닥부터 시작하는 데이터 과학 데이터 분석을 위한 파이썬 프로그래밍과 수학·통계 기초
by Joel Grus, Hangyeol Kim, Seongju Ha, Eunjung Park
Content preview from 밑바닥부터 시작하는 데이터 과학 데이터 분석을 위한 파이썬 프로그래밍과 수학·통계 기초
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from Scratch
281
19.11
예시: MNIST
MNIST
1
는 손으로 쓴 숫자들로 구성된 데이터셋이며 딥러닝을 처음 배우는 모
든 이가 사용하는 예시이기도 하다.
처리하기 까다로운 바이너리(
binary
) 형태로 데이터셋이 제공되기 때문에,
mnist
라이브러리를 사용할 것이다. (이 부분만큼은 밑바닥부터 시작하지 않을
것이다.)
python -m pip install mnist
이제 아래와 같이
MNIST
데이터를 불러올 수 있다.
import mnist
#
데이터를
지정한
경로에
내려받아
준다
.
# (
인자가
없도록
정의된
라이브러리의
함수이다
.)
# (
절대
이렇게
하지
않을
것이라고
했지만
람다
함수에
변수를
정의할
것이다
.)
mnist.temporary
_
dir = lambda: '/tmp'
#
각
함수에서
데이터를
내려받고
numpy
행렬로
반환
# .tolist()
를
사용해서
리스트로
변환
train
_
images = mnist.train
_
images().tolist()
train
_
labels = mnist.train
_
labels().tolist()
assert shape(train
_
images) == [60000, 28, 28]
assert shape(train
_
labels) == [60000]
첫
100
개의 학습 데이터가 어떻게
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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.