June 2018
Intermediate to advanced
304 pages
10h 7m
Chinese
Content preview from Deep Learning深度學習基礎｜設計下一代人工智慧演算法
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136
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第六章：嵌入和表達方式的學習
圖 6-14：我們對資料集進行破壞性操作，並訓練一個降噪自動編碼器，重建出原本未遭到破壞的
圖片
自動編碼器的稀疏性（Sparsity）
深度學習最困難的概念之一，就是「
可解釋性
（interpretability）」的問題。可解釋性是
機器學習模型的一個屬性，衡量的是模型的過程與輸出，究竟有多麼容易進行檢查和解
釋。深度模型往往很難以解釋，因為模型經常含有非線性的性質和大量的參數。雖然深
度模型所得到的結果通常比較準確，但如果缺乏可解釋性，通常會阻礙它在高度有價值
但風險極高的應用中被採用的機會。舉例來說，如果要用機器學習模型預測患者有沒有
癌症，醫生可能就需要得到具體的解釋，才能確認模型的結論是否可信。
我們可以研究一下自動編碼器的輸出特性，試著解決可解釋性這方面的問題。一般來
說，自動編碼器的表達方式是「稠密的（dense）」
—
這個說法隱含的意思，就是當我
們對輸入進行一連串相關修改時，表達方式隨之改變的方式。請考慮圖 6-15 的情況。
自動編碼器的稀疏性（Sparsity）
|
137
圖 6-15：「稠密的」表達方式會以一種難以解釋的方式，把多個特徵的訊息組合疊加在一起
自動編碼器往往會生成一種「
稠密的
（dense）」表達方式，也就是說，原始圖片的表達
方式會被高度壓縮。由於我們並沒有很多維度可做為表達方式，因此這樣的表達方式必 ...
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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.
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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.