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@@ -62,7 +62,7 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle
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- [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits.
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## Natural Language Processing
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-- [Reccurent Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task.
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+- [Recurrent Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task.
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- [Bi-Directional LSTM](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task.
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- [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network.
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- [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network.
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