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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第一章：機器學習
點積
在機器學習中，
點積
（
dot product
）是我們經常會遇到的一種線性代數運算方式。點積
有時也叫「內積（inner product）」或「純量積（scalar product）」。它是取兩個相同長度
的向量進行運算，然後得出單一個數字的結果。其做法就是把兩個向量中相對應的個別
項目逐一相乘，然後再全部加總起來，就可以得出最後的計算結果了。就算沒有特別說
明其中的數學含義，我們還是可以看出這個單一數字蘊含了許多訊息。
首先我們可以看到，點積可用來衡量每個向量中各個元素值的大小。如果兩個向量都具
有相當大的值，就可能得出相當大的結果，而如果只是具有相當小值的兩個向量，則只
會得出相當小的結果。如果在數學上要進行所謂「
歸一化
（
normalization
）」之類的處
理，往往需要計算向量之間的相對值，此時就可以運用點積運算，來做為向量之間相似
程度的一種衡量方式。針對兩個歸一化向量進行點積運算，這就是數學概念上所謂的
「
餘弦相似度
（
cosine similarity
）」。
元素級相乘
在實務上另一種常見的線性代數運算，就是所謂的「
元素級相乘
（element-wise
product）」（或叫做「Hadamard 相乘」）。這種運算是針對兩個元素數量相同的向量，把
每個相對應的元素兩兩相乘，最後得出另一個同樣具有相同元素數量的新向量。
外積
兩個向量的「外積（outer product）」，也叫做「張量積（tensor product）」。我們會取縱
列向量（column vector ...
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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.