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Drawing word2vec
1.
Drawing word2vec Kai Sasaki
2.
Training Algorithm CBOW Skip-gram Hierarchical Softmax Omit
Negative Sampling this time :(
3.
CBOW Continuous Bag of
Words Disregard grammar and work order Share the weight of each words Training around words
4.
CBOW syn0[i-1] syn0[i] syn0[i+1]
5.
CBOW syn0[i-1] syn0[i] syn0[i+1] neu1
6.
CBOW syn0[i-1] syn0[i] syn0[i+1] neu1 syn1 Use word on
the path to syn0[i] in huffman tree as network weight f
7.
CBOW syn0[i-1] syn0[i] syn0[i+1] neu1 syn1 Use word on
the path to syn0[i] in huffman tree as network weight f g g is the gradient that is calculated with (1 - syn1.code -f) * alpha This is error of output
8.
CBOW syn0[i-1] syn0[i] syn0[i+1] neu1 syn1 Use word on
the path to syn0[i] in huffman tree as network weight f g neu1e g syn1 Backpropagate to hidden layer
9.
CBOW syn0[i-1] syn0[i] syn0[i+1] f g neu1e g syn1 Backpropagate to
hidden layer Adding error back to each words in window
10.
CBOW syn0[i-1] syn0[i] syn0[i+1] f g neu1e g syn1 Backpropagate to
hidden layer Adding error back to each words in window
11.
CBOW syn0[i-1] syn0[i] syn0[i+1] f g neu1e g syn1 Backpropagate to
hidden layer Adding error back to each words in window
12.
CBOW syn0[i-1] syn0[i] syn0[i+1] They are the
conclusive continuous representations that are available through training
13.
Skip-gram Reverse format of
CBOW Predict representations of word that is put around the target words
14.
Skip-gram syn0[i] syn1 Use word on
the path to syn0[i] in huffman tree as network weight f
15.
Skip-gram syn0[i] syn1 Use word on
the path to syn0[i] in huffman tree as network weight f g g is the gradient that is calculated with (1 - syn1.code -f) * alpha This is error of output
16.
Skip-gram syn0[i] syn1 Use word on
the path to syn0[i] in huffman tree as network weight f g neu1e g syn1 Backpropagate to hidden layer
17.
Skip-gram syn0[i] f g neu1e g syn1 Backpropagate to
hidden layer Adding error back to each words in window
18.
Reference https://code.google.com/p/word2vec/
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