So so far, we are learning techniques from artificial intelligence, so all of these neural networks
are actually from artificial intelligence, but we can add very simple steps in order to make it deep.
Learning how we can do that.
Well, a deep neural network is nothing but a neural network, a regular one, but deeper, as you can
see here.
Usually we had only one hidden layer, but now we have multiple hidden layers in between that do the
calculations store features before outputting the results for us.
So it does store more data about the input.
Some of this will be activated.
Some of this will be not activated during training.
And well, for larger images, it makes perfect sense for larger data.
It makes perfect sense that you have a deeper network that can do deeper analysis.
OK, so the purpose of having a deeper network is we can do better decisions at the output.
OK, so as I said, the difference is very simple.
One of them is just one hidden layer and the other is has multiple Hitlers.
So this lecture is not an introduction to deep learning.
It's not.
But just to give you an idea that jumping from artificial intelligence to deep learning in the neural
network sense is all about adding a bunch of hidden layers, regardless of the complicated math that
comes along with it, regardless of all of that.
This is the plain basic concept here.