Lecture 2 - Simple Word Vector representations: word2vec, GloVe
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How to represent meaning in a computer?
Discrete Representation：
One-Hot Representation
But the one-hot representation has a problem: hard to compute similarity.
Distributional Representation：
Full Document & Window Based
Full document, like word-document coocurrence matrix -> LDA -> suitable for text classification.
Window based, like the following:
But the window based method also has a problem: the matrix dimension is too high.
Solution: SVD, 什么是奇异值分解 
But SVG has to cost much time!
So we think about obtaining low dimensional vectors directly.
Warm Up: Gradient Descent
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