Hello and welcome to this section on Convolutional Neural Networks.
Super excited that you're joining us for this section as well.
And today we're going to cover off the plan of attack.
How are we going to learn everything in this section?
There's so much to learn.
let's see how are we going to approach this.
All right. What do we learn in this section?
First of all, we'll talk about what convolutional networks actually are
very important to understand the end goal that you're working towards
before you actually start working towards it.
So what about features?
We'll have a look at a few little examples.
We'll compare the human brain to artificial neural networks
in terms of image recognition.
So it'll be a fun a light tutorial to get us started for this whole section.
Then we'll talk about step one diving straight into it convolution operation.
So this part of the course contains several steps that we need to go through
in order to build, a convolutional neural network.
And that's how these tutorials are going to be broken up.
So this one is going to be step one.
The convolution operation will learn everything about feature detectors.
We'll talk about which are also filters.
We'll talk about feature maps.
And you know, how what are the different parameters there, what they mean.
And have a look at some visual examples as well.
Then we'll talk about step one, part B, the ReLU layer or ReLU layer
which is the rectified linear unit.
And we'll talk about why linearity is not good
and how we want more non-linearity in our network for image recognition.
Then we'll talk about step two pooling.
And we'll understand, how pooling works.
We'll talk specifically about max pooling.
And we'll also mention a couple of things about a mean pooling or some pooling and,
other approaches that you can take to the process of pooling.
Also in this lecture we'll have a really cool example.
So there'll be a, very visual interactive tool that we're going to look at.
So make sure to stick around to the end of that lecture, because that's
going to add a lot of value to your learning process.
What we're going to discuss at the end there.
step three flattening.
So here we will.
It's going to be a quick tutorial
on how to proceed from your pooled layers to your flattened layer.
And then we're going to talk about a full connection.
So this is the the very, meaty tutorial that puts everything together
and puts everything into perspective
and actually shows you how everything works at the end of the day
and how those final neurons understand how to classify your image.
Very, very important tutorial.
and hopefully that will,
summarize or kind of, put everything together for you.
And finally we'll have a summary which will summarize everything
we've talked about.
And as an extra little feature, I've included
a tutorial on softmax and cross entropy.
So you don't have to take this tutorial, but I thought it'd be a great addition,
of knowledge, because these are terms that you will come across
when dealing with convolutional neural networks.
So, maybe, maybe take it right away.
Maybe, when you come across these terms, you can you will always know
you can come back to this course and take,
this tutorial to understand better what softmax and cross-entropy are.
And also, as always throughout, these, tutorials, there will be
lots of recommended reading for you to further upskill and get more knowledge.
And on that note, I can't wait to see you on the first tutorial!
This is going to be a very fun
and exciting section, and until next time, enjoy deep learning!