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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第八章：向量化
對於本書先前提過的原始文字理解
10
、推 薦
11
與 graph 圖模型化
12
等方面，Word2Vec 向量
表達方式都帶來了一些新穎的做法。關於如何構建 Word2Vec（與段落向量）的程式碼
範例，請參見第 5 章的內容。
graph 圖的處理
graph 圖結構（graph structure）的向量化，在資料科學流程的處理方面算是一個難題。
雖然我們並不會詳盡討論 graph 圖的向量化相關主題，但我們還是會討論一下 Node2Vec
這個變形的做法（另請參閱第 5 章）。
在使用 Node2Vec
13
的情況下，我們可以針對 graph 圖中的每個頂點（vertex）以及擴展
後的每個邊（edge）生成一個向量。我們可以像使用單詞向量（如前一節所提到的）那
樣，使用向量執行各種任務，例如進行節點分類，或是推測是否存在邊（或連線）。
Node2Vec 是源自於另一種稱為 DeepWalk 的 graph 圖方法，進一步通用化之後的結果
14
。
DeepWalk 的做法是透過在 graph 圖上執行截短式隨機漫步，來構建出關於圖結構的局部
訊息。這樣就可以讓 DeepWalk 透過把 graph 圖中的行程（trips）視為句子的等效表達
方式，來學習 graph 圖中的潛在表達方式。最後，我們想要指出一種運用「graph CNN
（
https://tkipf.github.io/graph-convolutional-networks/
）」的做法
15
，它是你應該特別留意的
一種做法，不過 ...
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