Hi and welcome back.
In this section, I'll dive into the history of CNN's basically, this is a very high level outline
just showing you a detailed timeline of how CNN's have progressed to evolution.
Just so we know the sequence of when these different networks arose and we can see where we are in our
modern day.
No, I should add this is this has been updated only to 2018.
And the reason for this?
Well, mainly because actually didn't update this, to be fair, but I will be discussing some of the
more modern architectures here.
But I'll go back to this.
The reason I stopped right here, and that's because RTÉ's next, which is a very good CNN network,
it's honestly still has some of the world class performance in image classification.
In fact, there was a 2001 paper that actually showed resonates still performed best, despite all the
fancy architectures that have arose over the years.
So residents are currently my favorite CNN if I had to choose.
But it was the second favorite because it really is just so good and reliable for many different image
recognition tests.
But we'll start at the bottom with this one.
Start with Linnet, which is another classical scene in architecture along with Alex Net.
And then we progressed to video, which came out in 2014, which was the year before I did my Masters
and II.
And I remember these students talking about big like it was so groundbreaking back then.
It doesn't seem that long ago, so you can see how fast things progressed in the deep, deep learning
world.
Next, we had inception and it was we wanted to do tree and current leaders before, but it also resonates.
We'll talk about resonance with groundbreaking resonance, helped residents onto a very complicated
architecture, which we'll discuss, though, and I'll discuss why they work so well.
But they were this of so many problems.
And then basically amidst this type of scene in architecture, so flexible and usable in image recognition,
class and classification and classification.
So also, we take a look at denseness exceptions shuffle that mobile that these were more efficient
models, not groundbreaking state of the art accurate models.
They were just very efficient.
So they were deployed on mobile devices and at a slower CPU type devices where computations computational
fluid mattered a lot.
Then we look at Inception Resonant Inception before RTÉ's next, which I discussed and mobile and that
fit two.
And since then, there's basically been a couple of things that have been happened.
Vision transformers have basically exploded in the last few months in the CNN world.
That's in 2020 to early 2020, a sort of late 2020, early 2021 when it first came on the scene.
And it's still exploding with research right now.
It is getting world class performance and we will discuss efficient transformers here.
I actually should have added it in this diagram.
I'm sorry, I didn't.
But either way, I will discuss it.
It's part of discourse.
Don't you worry, and I'll also say that rest next.
Right before that was still at that point, the last two or three is still getting groundbreaking performance.
So this has a lot that says that maybe we have stagnated on CNN research a bit coming down to the late
2018's early 2020 with no vision.
Transformers have basically progressed to field, and now we've got it getting again.
We're building state of the art schools with them.
So I'll stop talking about this right now and let's get into it.
So let's take a look at Linnet, which is the first classic CNN architecture that was made popular.
So I'll see you in the next section where we take a look at Linet.