April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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從生物神經元到人工神經元
|
279
輸入層
TLU
1
x
2
x
1
偏差神經元
（永遠輸出
1）
輸出層
輸入神經元
（轉傳）
Σ Σ
Σ
輸入
輸出
圖
10-5
這個感知器結構有兩個輸入神經元
，
一個偏差神經元
，
以及三個輸出神經元
拜神奇的線性代數之賜
，
公式
10-2
可一次性且高效地計算一層人工神經元處理多個實例
的輸出
。
公式
10-2
計算全連接層的輸出
在這個公式中
：
•
一如往常
，X
代表輸入特徵矩陣
，
在裡面每個實例一列
，
每個特徵一行
。
•
權重矩陣
W
含有所有連接權重
，
除了偏差神經元的那一個
，
在裡面每個輸入神經元一
列
，
階層的每個人工神經元一行
。
•
偏差向量
b
包含偏差神經元與人工神經元之間的所有連接權重
，
在裡面
，
每個人工神
經元有一個偏差項
。
•
函數
ϕ
稱為
觸發函數
（activation function），
當人工神經元是
TLU
時
，
它是步階函數
（
但是我們很快就會介紹其他的觸發函數
）。
感知器如何訓練
？ Rosenblatt
提出的感知器訓練演算法受
Hebb
法則
極大程度的啟發
。
Donald Hebb
在他
1949
年出版的書籍
The Organization of Behavior（Wiley）
中提出
，
當生
物神經元經常觸發另一個神經元時
，
這兩個神經元之間的連結會變得更強壯
。
稍後
Siegrid
Löwel
用一句朗朗上口的話歸納了
Hebb
的概念
「Cells that fire together, wire together」，
也
就是說
，
當兩個神經元同時觸發時
，
它們之間的連接權重就會增加
。
後來這條規則被稱
為
Hebb
法則
（
或
Hebbi
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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.