Update README.md

README.md

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 ### LSTM & GRU FOR RNN MODELS
 
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 ### WORD REPRESENTATION
 
 - 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.
+  * After having trained our Word2Vec or GloVe model (both contains E and W), we only use the embedding matrix E for transfer learning.