Hi and welcome back.
So in this section, we'll take a look at Alex Net, which was another advance CNN advanced for the
time, which was in 2012, and it was basically another classical CNN architecture.
And there's something I want to introduce here.
Well, firstly, others tell me who this guy is behind us.
It was Jeffrey Hinton's team from the University of Toronto.
The team called supervision, and the reason I have this graphic here, this image is because it was
the SVR winner.
That's the image net competition on this dataset.
Back then, it was a winner in 2012.
And that's how these guys and this network got famous.
Now I should I should mention that Alex is named after Alex Kristof Ski.
I hope I'm doing this name just as probably butchering the pronunciation as well as Ilya suits give.
Definitely.
But should that one, to be fair, but that's OK.
And Alex, not.
I'll talk about the architecture, but but I want to mention something else.
You would have noticed that Linnet came out in 1995.
Alex, not the next big CNN took like 15 years or 17 years before of CNN's got like back into in the
spotlight.
So why?
What?
What did what happened?
Well, remember A.J. Winters generally times and industry where there's a lot of hype and then a lot
of crap and a crashing?
Well, I went to between 2000 and 2010.
Roughly, it wasn't really a winter.
It was just a time when things sort of stagnated somewhat.
We weren't getting anywhere with neural nets at a time.
And the reason for that is we just didn't have the GPU computational power to really do a lot of experimentation
with them.
So a lot of researchers thought, yeah, neural nets are quite novel and quite good.
However, they're not practical in the real world because they're just too slow to train and require
too much data.
So it's stagnated, said the research back then.
However, things started to get a kick when GP use became a lot cheaper, a lot more powerful and and
a lot more libraries became accessible.
Python became more mainstream, and Python was so much easier to work with than C++ or Java when building
neural nets because of the libraries that accumulate even early libraries like coffee and theno were
actually quite easy to use.
To be fair.
So anyway, back to the architecture of Alex, and it contained eight layers, with the first five being
convolutional Liz and the Last Tree being FC layers.
So let's take a look at the diagram.
But before we do, we should mention that this was a big CNN back back then 60 million parameters,
which was quite large, in my opinion.
That's still quite large, even for networks at Atrium.
So it did take 2GB to use state of the art GPUs back then.
Probably some in video quadruples most likely took over a week to train, so it can see it for quite
some time.
And this is an illustration of the architecture.
It's not the best illustration it Kim Go got distracted from the people.
However, for some reason, the authors cropped half of the image, and it looks a bit weird, in my
opinion.
But you can kind of see the CNN layers here.
There's a scene and live here on CNN, live here, CNN live here and then here, I believe.
Yeah.
And then there's the FC layers, the three of them here.
So that's how we get this, this architecture.
So it's not a very complicated design.
Even though it has five layers, it's still a relatively simple CNN.
So now let's take a look at Viji, which is one of my favorite networks because it generally works so
well.
But it's a big network, so it does come with a cost.
So I'll see you in the next section.
Thank you.