# Does dropout regularization prevent overfitting due to too many iterations?

For image classification problem, let's say, and given a neural network to train on,

if you were to run too many iterations for a single image of a cat would not generalize well into other images of cats. But then, if you were to run only 1 iteration for a single image of a cat, then using the same weights of the network, you go through another iteration using another picture of a cat, then it would simply not converge fast enough since you wouldn't be able to use RMSprop....etc

So one way to prevent that is by using dropout regularization but is there a proof that even with so many iterations per example, it makes the network "difficult" to overfit for that each example?

• Also, when using batch normalization, do you normalize the batch after the dropout? I would assume so, in order to be able to do backpropagation – Kevvy Kim Oct 31 '18 at 23:43