How Does Regularization in a Neural Network | Dealing with overfitting Work?

Regularization is like giving your neural network a little extra training to stop it from getting too focused on one thing.

Imagine you're learning how to sort your toys by color, red, blue, green. If you only look at the colors of your toys and ignore their shapes or sizes, you might get really good at sorting just those few toys. But then someone gives you a new toy that's red but looks different from the others, and suddenly you're confused.

That’s what overfitting is, your network gets so used to one kind of example (like your favorite red block) that it can't handle anything new.

Regularization helps by gently pushing your network to not focus too much on any single detail. It's like telling your brain, "Don’t get too excited about the red color, there are other things to pay attention to, too."

Think of it as a teacher who gives you a little break from doing only one type of problem. Maybe they tell you to do something simpler or even skip a few steps sometimes.

In regularization, your network still learns, but it’s encouraged to keep things simple, like choosing the shortest path home instead of taking the longest way just because it’s more fun.

This helps your brain (or your network) remember better when new toys come along.