Introduction to Regularization to Reduce Overfitting of Deep Learning Neural Networks

The objective of a neural network is to have a final model that performs well both on the data that we used to train it (e.g. the training dataset) and the new data on which the model will be used to make predictions. The central challenge in machine learning is that we must perform well on new, previously unseen inputs -- not just those on which our model was trained. The ability to perform well on previously unobserved inputs is called generalization.