chore(readme): word2vec chart explained
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README.md
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README.md
@ -67,3 +67,31 @@ notMNIST dataset, sigmoid hidden layer, cross-entropy loss, learning rate decay
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notMNIST dataset, relu hidden layer, cross-entropy loss, learning rate decay and sgd ([`notmnist.hs`](https://github.com/mdibaiee/sibe/blob/master/examples/notmnist.hs)):
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notMNIST dataset, relu hidden layer, cross-entropy loss, learning rate decay and sgd ([`notmnist.hs`](https://github.com/mdibaiee/sibe/blob/master/examples/notmnist.hs)):
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![notMNIST](https://github.com/mdibaiee/sibe/blob/master/notmnist.png?raw=true)
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![notMNIST](https://github.com/mdibaiee/sibe/blob/master/notmnist.png?raw=true)
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### Word2Vec
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word2vec on a very small sample text:
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```
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the king loves the queen
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the queen loves the king,
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the dwarf hates the king
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the queen hates the dwarf
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the dwarf poisons the king
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the dwarf poisons the queen
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the man loves the woman
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the woman loves the man,
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the thief hates the man
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the woman hates the thief
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the thief robs the man
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the thief robs the woman
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```
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The computed vectors are transformed to two dimensions using SVD:
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`king` and `queen` have a relation with `man` and `woman`, `love` and `hate` are close to each other,
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and `dwarf` and `thief` have a relation with `poisons` and `robs`, also, `dwarf` is close to `queen` and `king` while
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`thief` is closer to `man` and `woman`. `the` doesn't relate to anything.
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![word2vec results](https://raw.githubusercontent.com/mdibaiee/sibe/master/w2v.png)
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This is a very small dataset and I have to test it on larger datasets.
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