August 2022
Beginner to intermediate
464 pages
10h 42m
Korean
Content preview from 개발자를 위한 머신러닝&딥러닝
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283
11
장
시퀀스 모델을 위한 합성곱 신경망과 순환 신경망
여기서 보듯이 전체 예측을 향상하기 위해 여러 가지 다른 신경망 구조와 다른 하이퍼파라미터
를 시도해볼 수 있습니다. 최상의 선택은 데이터에 따라 다릅니다. 따라서 여러분이 가진 특정
한 데이터셋으로 문제를 해결할 때 이번 장에서 배운 기술이 유용할 거라 장담합니다!
11.7
마치며
이 장에서 여러 종류의 신경망으로 시계열 데이터를 예측하는 모델을 구축해보았습니다.
10
장
에서 만든 간단한
DNN
에 이어서 합성곱 신경망과
RNN
,
GRU
,
LSTM
같은 순환 신경망을
다루었습니다. 하이퍼파라미터와 신경망 구조를 조정해 모델의 정확도를 향상하는 방법을 살
펴보았고, 수백 년의 온도 측정값으로 구성된 대용량 실전 데이터셋으로 연습해보았습니다. 이
제 여러분은 다양한 데이터셋에서 신경망을 만들 준비가 되었고 최적화에 필요한 사항을 잘 이
해하고 있습니다!
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.