May 2020
Beginner to intermediate
952 pages
24h 11m
Korean
Content preview from 핸즈온 머신러닝(2판): 사이킷런, 케라스, 텐서플로 2를 활용한 머신러닝, 딥러닝 완벽 실무
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383
10
장
케라스를 사용한 인공 신경망 소개
이킷런의
fetch
_
california
_
housing
()
함수를 사용해 데이터를 적재하겠습니다. 이 데이
터셋은 수치 특성만 있으므로
2
장에서 사용한 것보다 간단합니다 (
ocean
_
proximity
특성이
없습니다 ). 누락된 데이터도 없습니다. 데이터를 적재한 후 훈련 세트, 검증 세트, 테스트 세트
로 나누고 모든 특성의 스케일을 조정합니다.
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
housing = fetch_california_housing()
X_train_full, X_test, y_train_full, y_test = train_test_split(
housing.data, housing.target)
X_train, X_valid, y_train, y_valid = train_test_split(
X_train_full, y_train_full)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_valid ...
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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.