training deep leaning models model performance training accuracy training precision training recall training rocauc validation accuracy validation precision validation recall validation rocauc we defined the number of epochs as and the batch size as while keeping weight_decay= and lambda_l = (which essentially removes the effect of the and regularizerswe will experiment with these values soonas in we used the adam optimizer with bceloss(for our network our network has three hidden layerswith and neuronsrespectively we can play around with different sizes of units within the neural network architecture if we take closer look at the results between the training and validation datasetswe can see huge gap single metric that helps in capturing this difference is roc auc (area under curve)we have auc as %as opposed to for training and validation this gap is huge essentiallywe are facing the overfitting problem to overcome overfittingwe would need to add regularizers that would add penalty to the model' losscueing the model to learn simpler patterns ideallywe would want to see similar results between training as well as validation