January 2019
Beginner to intermediate
576 pages
14h 31m
Chinese
Content preview from 深度學習｜內行人的做法
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第二章：神經網路和深度學習基礎
人工智慧的第二個寒冬：
1990-2000
年代初期
在 1990 年前後那段期間，所謂的「專家系統（expert systems）」 和
「LISP 機器」這些技術被人們過度推銷，結果到最後兩者都未能達到人
們的預期。在這段期間的最後，「戰略運算計劃（Strategic Computing
Initiative）」終究取消了新的預算支出。所謂的第五代電腦，最終還是未
能實現其目標。
激活函數
我們使用激活函數，把每一層節點的輸出，正向傳送到下一層，直至抵達輸出層為止。
激活函數是一種純量轉純量的函數，專門用來計算神經元的激活值。我們之所以在神
經網路的隱藏神經元中使用激活函數，目的就是要把非線性引入到網路的模型化能力之
中。有許多激活函數屬於邏輯類轉換，如果畫成圖形，看起來會有點類似 S 的形狀。這
類函數就稱為「
S
型
（
sigmoidal
）」的函數。S 型這類的函數有很多種變形，其中一種就
叫做「S 型函數（Sigmoid function）」。現在我們就來看看神經網路中一些很有用的激活
函數。
線性函數
線性轉換（參見圖 2-11）基本上是一種恆等函數（identity function）， f(x) = Wx，其中
因變數與自變數具有直接的比例關係。實際上，這也就表示函數直接把信號往後送而不
做改變。
我們可以看到這種激活函數，經常被用於神經網路的輸入層。
S 型函數
就像所有的邏輯轉換函數一樣，S 型函數可以降低資料中極端值或異常值的影響，而不
需將其刪除。圖 2-12 ...
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