title: LSTM for Regression (in Tensorflow)
body: <p>I want to implement some LSTM model in Tensorflow. I think I understood the tutorials fairly well. In those input data was given in the form of words, which were embedded into a continous vector space (which has several advantages). 
  I now want to make an LSTM to predict a series of contionous numbers and do not know what is the best approach to that. 
  Should I discretize my input range, thus effectively get a classification problem with a number of classes and use the embedding desribed before, or stick to the continous numbers and do regression? In that case I just in each time step pass one feature to the model, namely the continous number?</p>