What happens if I train a network for more epochs, without using early stopping? I have a question about training a neural network for more epochs even after the network has converged without using early stopping criterion.

Consider the MNIST dataset and a LeNet 300-100-10 dense fully-connected architecture, where I have 2 hidden layers having 300 and 100 neurons and an output layer having 10 neurons.

Now, usually, this network takes about 9-11 epochs to train and have a validation accuracy of around 98%.

What happens if I train this network for 25 or 30 epochs, without using early stopping criterion?
Training a neural network for "too many" epochs than needed without using early stopping criterion leads to overfitting, where your model's ability to generalize decreases.