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深度學習筆記有深度學習領軍人物個人主頁

             **為什麼深度的神經網路比較好?這個問題在《Learning Deep Architectures for AI》提及過。原文為“The main conclusion of this section is that functions that can be compactly represented by a depth k architecture might require an exponential number of computational elements to be represented by a depth k − 1 architecture. Since the number of computational elements one can afford depends on the number of training examples available to tune or select them, the consequences are not just computational but also statistical: poor generalization may be expected when using an insufficiently deep architecture for representing some functions.” 解讀為存在某些函式可以簡潔地通過 k 層邏輯閘網路計算出來,但是如果限制為 k -1 層的話,就需要指數級別的邏輯閘才行(解讀來自
點選開啟連結
)。其實可以這麼認為:如果不深,那麼想達到同樣效果的神經網路就必須很扁平,並且用到的引數個數是前者的指數級(現在深度神經網路的引數個數是million級別)。引數一劇增,各種問題就出現了。