Improving Model Performance

In order improve the performance of a neural network model, a number of ways have been studied. In this chapter, minibatch and k-fold cross-validation are explained. The basic idea of these two methods is on controlling the dataset, since repeated usage of the same dataset for training and validation might result in overfitting. Furthermore, regularization of the neural network model training by L-norm regularization and dropout of hidden nodes are explained in this chapter to avoid overfitting.