Welcome back to the course in this section, we'll take a look at the grandfather of CNN's and that's
the line at CNN architecture.
So let's get started.
So firstly, why was Lynnette developed?
Well, the US Postal Service had a problem.
They had a problem in automatically reading these digits, these handwritten digits when people write
their mailing address on the letters or packages.
They needed a way to read these numbers accurately, and that was the whole genesis of why that was
developed for this problem.
It was actually first introduced in 1995, and it got published and publicly announced an open sourced
in nineteen nine, I believe.
But the initial CNN was developed in 1995 by Liqun.
It was developed through handwritten digits, and you may have known this by now, but the amnesty to
said was basically collected from the U.S. Postal Service data.
So all those experiments we were doing with Amnesty, the set that was basically collected from the
first well on the first CNN, obviously, but it was collected from one of the first big data sets that
were used in computer vision and neural networks.
Pretty cool, huh?
So now let's take a look at a little architecture.
So this should look familiar.
Here we have an input image.
We have six feature maps.
We have a filter size that is actually whereas five by five.
So not that big, not that small, because we've used too much and filters previously.
Then we have a max pool.
If you can see the dimensions shrink by two, so have the six feature maps here.
Next, we have 16 filters here filters and these are file size five by five.
This is the output size here, and then we shrink it by having a next max pool.
In a max boot is a boot max.
Players have to buy two kernels strike two and then we have to fully connected layers.
So we actually have two fully connected layers.
We have 120 units here where neurons and then 80 for neurons here before finally it goes to the output
here.
This is basically a description of that same architecture.
So I wouldn't read it out.
But just so you know, this simple architecture with just six filters here and 16 filters here was able
to get ninety nine point three percent accuracy on the amnesty, was it?
And that's pretty cool.
Now let's take a look at this.
This is an actual demo of the Linnet architecture.
Back then, you can see as numbers slide across here, it's been classified on top here, so you can
see what they are.
So this is a pretty cool demonstration, isn't it?
So we'll stop there for now because that's basically all there is to to then.
That's not a very complicated network, as you've seen throughout a simple it's rather similar to the
ones we've done before, actually.
Now we'll take a look at it.
So I'll see you in the next section.
Thank you.