Generalization in Neural Networks

Whenever we train our own Neural Networks, we need to take care of something called the generalization of the Neural Network. This essentially means how good our model is at learning from the given data and applying the learnt information elsewhere. When training a neural network, there's going to be some data which the Neural Network trains on, and there's going to be some data reserved for checking the performance of the Neural Network. If the Neural Network performs well on the data which it has not trained on, we can say it has generalized well on the given data. Let's understand this with an example.