title: How to clarify which model layers to use for machine learning?
body: <p>We are currently doing a little experiment with machine learning with Deeplearning4j.</p>
  
  <p>We have voltage measurements in time series from different devices that I know that depends on each other. 
  We manage to labeling huge amount of those data with one and zeroes.</p>
  
  <p>Our problem is to figure out the use of layers for the model.</p>
  
  <p>For us it seems that it is experience that it is used among people and examples seems to be random.</p>
  
  <p>We currently using LSTM and RNN</p>
  
  <p>But how can we clarify if there is better models?</p>
  
  <p>We would like to see if the model can figure out some dependencies through predictions that we haven’t noticed.</p>