Neural Network for Multiple Float Output I have a dataset containing 40 input columns and 12 output columns (float values).

I supposed it's a regression problem and I am wondering of how to choose the ideal architecture of the Neural Network i didn't find examples of neural Network regression with multiple output 
What is the right way to do it?
Just use an output layer with 12 neurons instead of 1. Qualitatively there is no difference. For regression the output activations should be linear, and you have a few choices for cost: RMSE, MAE, or Huber Loss to name a few.