So now we take a look at regularisation methods.
These are methods that help us prevent or reduce overfitting.
So let's get started.
So firstly, as I said, regularization is a technique that helps attempt to reduce overfitting and
basically improve the generalization of your model.
It allows us to complete control the model complexity so that we ensure the model is using the right
features to classify the object.
As you can see in this example, here we have we previously looked at we don't want a model that is
learning that the green grass belongs to a dog.
When all these images have dogs in them, we want them to learn what a teal knows is and motors.
So there are many ways we can do that.
So we'll take a look at these methods now basically in computer vision.
These are the most common regularization methods that we use can use L1 L2 regularization.
You can use data augmentation, which is something I actually use quite a bit in my deep learning models.
You can use drop, which is something I actually always use.
It's a very easy to implement, at least stop.
It's essentially a regularization technique per say.
It's just a good.
It's a way of knowing when to stop creating a model so that we actually don't continue to over fit on
data called early stopping.
And then there's another technique that we can use in between layers called batch normalization.
So it will stop there and we'll go into each of these techniques in detail or bit of detail.
So we'll first start with L1 L2 regularization.
When we've gone through all of these techniques, we'll go back to a code and we'll train.
Some models firstly, will train them without any regularization and then we'll train them with what
with to regularization a few of the techniques combined and we can compare their performance.
So I'll see you in the next lesson where we take a look at L1 L2 regularization.