Hello and welcome to the section on convolutional neural networks.
Super excited that you're joining us for this section as well.
And today we're going to color 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 we're going to approach this.
All right what we learn in the 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 we'll hear what features will have a look at a few little examples.
Will compare the human brain to artificial neural networks in terms of image recognition.
So it'll be a fun light tutorial to get us started for this whole section.
Then we'll talk about Step 1.
Diving straight into it can volution 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 internals are going to be broken up.
So this was going to be step one the evolution operation we'll learn everything about feature detectors.
We'll talk about which are also filters.
We'll talk about future maps.
And you know how what are the different parameters they're what they mean and have a look at some visual
examples as well.
Then we'll talk about Step 1 Part B the realm you Lehre or really layer which is then rectified leaner
unit and we'll talk about why linearity is not good and how we want more nonlinearity in our network
for image recognition.
Then we'll talk about Step 2 pooling and will understand how pooling works we'll talk specifically about
Max pooling and we also mention a couple of things about I 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 will 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
flatten lair and then we're going to talk about full connections.
This is a 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 Umich very very important.
Tauriel 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 soft Max and cross entropy.
So you don't have to take this tutorial but I thought it would 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 soft Max and cross
entropy are.
And also as always throughout these editorials 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 on the first tutorial this is going to be very fun and exciting.
S. And until next time enjoy deep learning.