training deep leaning models is the santander group' value prediction challenge (kaggle com/ /santander-value-prediction-challenge/ good choice of loss function would be rmsethe activation for the output layer would be linearand the performance metrics choice can be rmse or mse ummary this covered the process of model training we also described number of critical steps and analyses that should be systematically performed in order to improve the model we also covered regularization techniques commonly used in deep learning--namelynorm penalties and dropout there are several other advanced/domain-specific techniques found in literature that must be mentioned so farwe have covered feed-forward neural networks and all the essential bits around deep learning using toy dataset and practical datasetas well as combination of the two with business use--case you should now have much more intuitive understanding of formulating use casedefining relevant metrics to benchmark modelsevaluating model performanceand evaluating the business viability in the next we will explore one of the most important topics within deep learning--convolutional neural networks--and embrace the field of computer vision