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I would like to fit an LSTM model using MXNET in R for the purpose of predicting a continuous response (i.e., regression) given several continuous predictors. However, the mx.lstm() function seems to be geared toward NLP as it requires arguments which don't seem applicable to a regression problem (such as those related to embedding).

Is MXNET capable of this sort of modeling and, if not, what is an example of an appropriate tool (preferably in R)? Are there any tutorials relevant to the problem I've described?

Josh
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LSTM is used for working with temporal data: text, speech, time series. If you want to predict a continuous response, then I assume you want to do something similar to time series analysis.

If my assumption is correct, then, please, take a look here. It gives quite a good example on how to use MxNet with R for time series on CPU. The GPU version is also available here.

Sergei
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