recurrent neural networks epoch train loss train predicted correct train denom percaccuracy valid loss valid predicted correct val denom percaccuracy epoch train loss train predicted correct train denom percaccuracy valid loss valid predicted correct val denom percaccuracy epoch train loss train predicted correct train denom percaccuracy valid loss valid predicted correct val denom percaccuracy as you can seethe performance has improved lot we trained the network for only five epochsyet the results are impressive readers are recommended to experiment by making changes to the network experiments could include changing the pretrained vectors (probably glove instead of fasttext)processing more nlp-related actions on the input dataadding more aggressive dropoutsadding more epochsetc this concludes our second exercisein which we tried to improve the performance of our sequence model we used the vanilla rnn networkslstm networksand bidirectional networks we also leveraged pretrained embeddings for numericized representations of words (this is highly recommended for almost all nlp-related tasks there also exists gated recurrent units (grus)which are very similar to lstms but which are slightly on the faster end of computationas they have fewer operations when it comes to performancehowevermost researchers have found both lstms and grus to be very similar in nlp experimentsit is very