-: Hello and welcome to the section
on Convolution Neural Networks.
Super excited that you are joining us
for this section as well.
And today we're going to cover off the plan of attack,
how we're going to learn everything in this section,
there's so much to learn.
Let's see how we're going to approach this.
All right, what we'll learn in this section.
First of all,
we'll talk about what convolution networks actually are.
Very important to understand the end goal
that you're working towards
before you actually start working towards it.
So we'll talk 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 convolution neural network
and that's how these tutorials are going to be broken up.
So this one's gonna be step one,
the convolution operation,
we'll learn everything about feature detectors,
we'll talk about, which are also filters,
we'll talk about feature maps
and what are the different parameters there, what they mean,
and we'll have a look at some visual examples as well.
Then we'll talk about step one part B, their 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 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 gonna be a quick tutorial
on how to proceed from your pooled layers
to your flattened layer,
and then we're going to talk about full connection.
So this is 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 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 in 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 convolution neural networks.
So maybe take it right away.
Maybe when you come across these terms,
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 upscale and get more knowledge.
And on that note,
I can't wait to see you on the first tutorial.
This is gonna be a very fun and exciting section.
And until next time, enjoy deep learning.