Hi and welcome back to the course.
So what I'm going to do in this chapter is give you a general introduction to convolutional neural networks.
It's going to be a gentle overview and it hopefully develops your intuition and understanding of CNN's
because we're going to go into CNN's and quite a bit of detail over the next, maybe about an hour of
video lectures.
So before we start, we need to ground you in your understanding of it.
So let's take a look at images.
What are images?
Let's take a look at this cat.
You know it's a cat, right?
What about this?
You know it's a dog.
But what do what did we see in the images that told us that this was a cat and this was a dog?
You can use you as a human who has learned the differences between cats and dogs from a very young age.
You know what a cat looks like?
It has these whiskers, these these big eyes.
This is, you know what?
The shape of a cat.
And you know, dogs are quite distinct.
So there are many things that told us it was a cat or dog.
Things like whiskers, sheep's eyes, flicker color, all of the things we went through.
But how does a computer or an algorithm or some software do this?
How does it know whether it's a cat or a dog?
How do we predict this from an image?
That's what convolutional neural networks allow us to do.
These are neural net is these are a type of neural networks that allow us to feed image images in and
get a class output out of it.
And that's the most basic sense of what it is.
So let's build some intuition of how CNN's work so you can see this here.
This looks like the number one.
It actually is number one.
Handwritten digit here.
So what digit does it?
It's the number one.
You know that you know the digits from one to 10.
You know, all of the symbols in all the letters, you know, a lot of things.
So how did you actually know what what it was?
So how would you tell a computer to actually figure out what is it?
Was it the overall shape?
Maybe it was something else.
So what if maybe it lies only in this region?
This, then it's a one.
But there are many issues with that, because what if most of the slides as a one?
But it was a piece that was outside here, like a seven or eight different shape, you know?
And you know, that can happen.
So what else?
What if it was shifted?
What if we shifted it out of this region?
Then you can.
Then you know, it's not.
There's no longer one in your programming and you're hardcoded looking for a one window here.
So how did how does a CNN, how does a neural network able to do this?
Well, that's how basically neural networks or convolutional neural networks have many filters, and
these filters allow us to extract features from different parts of the image.
Here, I'll explain this to you and in the later chapters.
But for now, think about this go back to the number one in your head.
I can go about this in the slide, but I still have to go through all of these here.
But think of the number one there.
Imagine we had a filter that was looking for only like a thin, narrow, vertical shaded region.
So imagine you have a specific filter and by filter, I mean like merging a little sliding window so
that that when it goes over those pixels, it activates that's exactly what happens in neural networks.
So imagine we have hundreds or thousands of these little filters, each little little filter looking
for specific features in an image.
And depending on which of those filters to run on, then that depends on the class.
So you can see it outputs probability in the end of what the images.
In this case, it's a cat.
So that should ground you in a basic understanding of what CNN's are.
This is a very long chapter.
So we're going to do.
We're going to do a lot of detail on CNN's starting with what a convolutions than what a feature detectors,
how treaty filters work when you do competitions on 3D images, what's it like or color images?
We're going to take a look at Colonel sidestepped patting stride activation activation littlies, which
is the redo function pulling fully connected layers soft max.
Then we're going to take a look at putting it all together to build the CNN.
And then we're going to take a look at how we get our parameter counts from a CNN.
And then we'll discuss why CNN's looks at wealth images and then we go into the training process, which
is the inner workings nitty-gritty, the make CNN's come alive.
We talk about loss, lost functions and back propagation gradient descent, and then we take a look
at some advanced things like optimizes and learning read schedulers.
And then we do a summary of students in the end.
So that's it for this chapter.
We'll see you in the next chapter where we discuss convolutions, so I'll stop there for now and I'll
see you in the next section.
Thank you.