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README.md

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-license: apache-2.0
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+license: apache-2.0
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+### LSTM & GRU FOR RNN MODELS
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+### WORD REPRESENTATION
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+- Word2vec & GloVe
+  * Word2vec includes two types of models, which are CBOW & Skip-Gram
+  * every sliding window, we calculate the gradients of both context and target words, so we could update our theta_t and e_c vector. After sliding all the windows, we could get embedding matrix E and prediction matrix W.
+  * After having trained our Word2Vec or GloVe model (both contains E and W), we only use the embedding matrix E for transfer learning.