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A couple of points: Have you firstly scaled your data, e.g. using MinMaxScaler? This could be one reason why your loss readings remain high. Additionally, consider that while Dropout can be useful for reducing overfitting, it is not necessarily a panacea. Let's take an example of using LSTM to forecast fluctuations in weekly hotel cancellations. Model ...


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As you know, an LSTM language model takes in the past word and tries to predict the new one and continue over a loop. A sentence is divided into tokens and depending on different method, the tokens are divided differently. Some model maybe character based models which simply uses each character as input and output. In this case you can treat punctuation as ...


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Take the sentence that was generated by your LSTM and feed it back into the LSTM as input. Then the LSTM will generate the next sentence. So the LSTM is using it's previous output as it's input. That's what makes it recursive. The intial word is just your base case. Also you should consider using GPT2 by open AI to do this. It's pretty impressive. https://...


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You can do custom POS Tagging and use it as a multi featured sequence2sequence.


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TL;DR Here is a beautiful explanation with diagrams: source To address: the cell state is essentially long term memory embedding (correct me if I'm wrong) The embedding can be long or short term and it is a vector. To answer: Why is the previous hidden state, current input and the bias put into a sigmoid function? Is there some special ...


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