April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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482
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第十五章：使用 RNN 和 CNN 來處理序列
遞迴神經與階層
截至目前為止
，
我們都只關注前饋神經網路
，
它的觸發只往一個方向流動
，
從輸入層到
輸出層
（
附錄
E
有一些例外
）。
遞迴神經網路看起來很像前饋神經網路
，
不過它也有指向
後方的連結
。
我們來看一個最簡單的
RNN，
它是由一個神經元組成的
，
這個神經元會接
收輸入
、
產生一個輸出
，
並將輸出送回去給它自己
，
如圖
15-1（
左
）
所示
。
這個
遞迴神
經元
在各個
時步
t（
也稱為
frame）
接收輸入
x
(t)
以及它自己上一個時步的輸出
y
(t–1)
。
因為
第一個時步沒有上一個輸出
，
所以它通常會被設為
0。
我們可以用時間軸來表示這個小網
路
，
見圖
15-1（
右
）。
這種做法稱為
在時間軸上展開網路
（
在每一個時步展示同一個遞
迴神經元
）。
時間
圖
15-1
遞迴神經元
（
左
）
在時間軸上展開
（
右
）
你可以建立一層遞迴神經元
，
在每個時步
t，
每個神經元都會接收輸入向量
x
(t)
以及上一個
時步的輸出向量
y
(t–1)
，
見圖
15-2。
注意
，
現在輸入和輸出都是向量
（
只有一個神經元時
，
輸出是純量
）。
時間
圖
15-2
遞迴神經元階層
（
左
）
在時間軸上展開
（
右
）
遞迴神經與階層
|
483
每一個遞迴神經元都有兩組權重
：
一組是輸入
x
(t)
的
，
另一組是上一個時步的輸出
y
(t–1)
的
。
我們將這些權重向量稱為
w
x
和
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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.