So now that we have completed your introduction to regularization and you've seen how well it works
in reducing overfitting and improving generalization, let's take a look at some guidelines on when
and how to use regularization.
So let's get started.
So firstly, it's always a good practice to train without regularization for this, and I'll tell you
why.
Firstly, sometimes it regularization techniques or combinations of them can have detrimental effects
on a model of performance.
Example, if you use some bad parameter settings, you can actually make the model performance worse
and the test data set.
So generally, it's always, always good to have a baseline model and then, but we introduce different
regularization techniques to assess the impact.
Also, it's never a good idea to start with drop out in that storm when you're trying to experiment
because this increases the convergence time.
So it's going to basically slow down your own training experimentation process.
So here are some tips and warnings when using regularization methods.
Dropout.
Firstly, don't use it before the final soft max layer, you can use it.
The other points where I showed you previously, which is after the Congolese or the max pool, is so
when training with El to drop our data augmentation, you need more ebooks to achieve the same performance
generally.
So you always have to add more ebooks when adding on to these when introducing these methods.
Now here's a note that I've mentioned before that things like data augmentation, dropout, batch gnome,
they're quite slow.
They add extra computational processing during the training process, and as such, they will slow it
down.
So it's a double double impact anyway.
Now, unbefitting is possible, too, as well if your L2 weights are set too high.
That's not a concern.
So be careful with how you set these parameters.
We'll take a look at some using some of these in in the next section where we start using regularization
techniques and keras and PyTorch.
So stay tuned for that.
So in the next lesson, we'll take a look at actually implementing some of these regularization techniques
in court.
So I'll see you in the next lesson.
Thank you.