Hello and welcome back to the course on artificial intelligence.
And today's section we're going to tackle deep convolutional CULE learning.
So we're taking a deep learning too even a further step so we originally started to cool earning the
simple learning.
Then we took that to deep learning and now we're taking too deep convolutional learn.
So let's see what we're going to discuss in terms of intuition.
Different sections are going to be quite quick.
There's not much that we need to add as long as we're familiar with convolutional neural networks and
we'll touch on this towards the end of this trial.
So today in this section we're going to talk about deep relationship learning the intuition behind things
and why it's so powerful why exactly it's so important to move away from deep Kule learning and why
deep learning is just a basic building block where it's just a step for us on the postes to deep relationship
learning and what kind of avenues deep can Lucian convolutional CULE learning opens up to what kind
of avenues the knowledge opens up to and where it can be applied.
We'll have some examples of that and then we'll talk about eligibility.
Trace or end step learning a very powerful addition to the whole concept of deep learning.
And we'll talk about the intuition behind that.
It's quite a complex topic but nevertheless we'll break down the intuition in quite simple terms and
then I'll give you some additional references where you can read up about eligibility Tracy if you'd
like to go into more detail but it is important for us to get the intuition down pat because we're going
to be using that in practical terms because we're delving into much more complex topics now that we
need to be add these extra or extra elements to our agents or to our learning algorithms so that they
can actually handle these complex environments and navigate them successfully.
And of course in this section because we're talking about convolutional neural networks it is highly
advisable that you check out an x number to convolutional neural networks.
Once again if you've done deep learning aitches at course then you already you're already familiar with
this information so you can safely proceed with these tutorials on the deep emotional Kule learning.
If you haven't done the deep learning aitches of course then it's a great idea to look at convolutional
neural networks and look at those intuitions.
Charles there so you understand better how images are processed by neural networks in order to look
for features and what's the whole convolutional layers are about pooling Lares the flattening layers
and how all that works in order to come up with some parameters about that describe the environment
or that describe that image and therefore we're going to be using those as inputs into our neural networks
instead of that vector which we're talking about.
But more on that in the next tutorial.
So if you haven't seen those controls yet we will advise you to check them out to get up to speed with
or refresh knowledge on Cullerton illusional neural networks.
All in all we've got an exciting section and as you can see what that many intuition Tournelles meaning
that you can.
You'll be able to jump into the practical side of things very quickly.
So on that note I've always seen the first tutorial.
And until then enjoy AI.