training deep leaning models overallwe see similar performance in the above three scenarios in an ideal experimentthere are no defined benchmarks that we could use for selecting the type of regularization that would work better we would need to experiment with different types of regularizers as well as different values for the hyperparameterslambda regularization and with hyperparameter values of ( , , , )dropout layer with values ( , etc with results from all the experiments in placewe would be more informed about which type of regularization works best for the data nterpreting the business outcomes for deep learning the results are fairly good we see small gap between training and validation performance (refer to the gap between red and blue line within the roc plot overallwe have accuracy on the validation datasetwith precision at and recall at these results are very encouraging out of predictions made as "yesfor marketing campaign outcomewe are times correct while covering of all customers who would positively respond to the campaign