https://youtu.be/RHvROP94qZ0
AI邊緣運算實作: TensorFlow Lite for MCU
https://bit.ly/3j2fIIt
[1]python程式設計
https://bit.ly/359cz4m
[2]AI機器學習&深度學習
http://bit.ly/2KDZZz4
[3]TensorFlow Lite for MCU
https://bit.ly/3j2fIIt
12. 資料間的相似程度 (Similarity)即計算它們的特徵
距離
Distance Metrics:
特徵距離的計算
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2.) Euclidean Distance:
1.) Manhattan Distance:
x = (x1, x2, x3,..., xn) and y = (y1, y2, y3,…, yn)
n-number of features xi and yi are the features of
vectors x and y respectively, in the two dimensional
vector space.
3.) Cosine Distance:
14. Min-Max Normalization
• Re-scaling the range of a vector to make all elements lie
between 0 and 1
Z-score Standardization
• Subtract the mean and divide by the standard deviation
Feature re-scaling
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將每個特徵值的尺度轉成一致
~(0,1)
Min-Max Normalization使用時機,
資料的上下界通常是己知的固定值
17. The Cosine metric is a measurement of orientation
and not magnitude
• Cosine不看magnitude(強度),只在乎2個向量是否具有相
同方向 (且不一定要有相同向量空間).而Euclidean是要是
相同向量空間且magnitude會影響計算的距離.
• Cosine Similarity 常用在文章分類, 因為文章出現關鍵詞
種類愈多比較重要,而不是某一個個關鍵詞出現很多次
(因為很有可能出現的很多次, 其實只是這篇文章寫得比
較長而已)
Use Euclidean Distance or Cosine Similarity ?
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