January 2017
Beginner to intermediate
312 pages
7h 3m
Korean
Content preview from 밑바닥부터 시작하는 딥러닝
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89
3
장
-
신경망
출력층의 활성화 함수는 풀고자 하는 문제의 성질에 맞게 정합니다. 예를 들어 회귀에는 항등 함수
를,
2
클래스 분류에는 시그모이드 함수를, 다중 클래스 분류에는 소프트맥스 함수를 사용하는 것이 일반적입
니다. 출력층의 활성화 함수에 대해서는 다음에 다시 자세히 설명합니다.
3.4.3
구현 정리
이로써
3
층 신경망에 대한 설명은 끝입니다. 그럼 지금까지의 구현을 정리해보도록 하죠. 신경
망 구현의 관례에 따라 가중치만
W1
과 같이 대문자로 쓰고, 그 외 편향과 중간 결과 등은 모두
소문자로 썼습니다.
def init
_
network
():
network
=
{}
network
['
W1
']
=
np
.
array
([[
0
.
1
,
0
.
3
,
0
.
5
],
[
0
.
2
,
0
.
4
,
0
.
6
]])
network
['
b1
']
=
np
.
array
([
0
.
1
,
0
.
2
,
0
.
3
])
network
['
W2
']
=
np
.
array
([[
0
.
1
,
0
.
4
],
[
0
.
2
,
0
.
5
],
[
0
.
3
,
0
.
6
]])
network
['
b2
']
=
np
.
array
([
0
.
1
,
0
.
2
])
network
['
W3
']
=
np
.
array
([[
0
.
1
,
0
.
3
],
[
0
.
2
,
0
.
4
]])
network
['
b3
']
=
np
.
array
([
0
.
1
,
0
.
2
])
return network
def forward
(
network
,
x
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.