Hello, guys, and welcome back.
So far, we have created all of our environment, we created a virtual environment, we put all of our
libraries and dependencies on them.
We have also installed Tenso, Floor and Kiraz.
We have linked our interpreter, which is this genie with the virtual environment so that we can access
everything that we have downloaded so far.
We've done a lot so far in order to prepare to write programs for deep learning.
We still need one more step.
And this step is about the application that we are going to create.
So the application that we will create is about having multiple images of digits, digits between zero
and nine.
And we are going to pass them to our deep learning network, our CNN, and they will be able to recognize
that the handwritten digits and tell us what digit it is.
OK, so in order to do that, we need to download this huge amount of images about that are related
to numbers.
And there was a library, an image library that is called this is very popular in order to learn about
deep learning, because it gives you an easy starting point.
It's really not a very hard data set.
It is a train.
And once you learn to do that, you can just grab your own data set and pass it to the same deep learning
algorithm, maybe with a little bit of tweaks.
And you should be able to build any deep learning model you want that is used in order to classify images.
OK, so this process of choosing which digit it is by giving the deep learning algorithm and image is
called the classification.
OK, so where are we going to get these images from?
Well, let us go to the Web browser and I'm going to type minced JPEG guitar.
And here we have this defensed, he has uploaded all of these images into his guitar proposal so we
can just go and get it, just click on go to file, click on this meme's dataset chebeague and on this
download, right.
Click and copy link adris.
Now we could just click, download and get the file here, but I want to teach you how to download files
from Basche or from the terminal right here.
I mean, it's very easy to do that here, but I won't be teaching you anything if I tell you just to
do that right.
We want to learn some new stuff about Linux in general.
All right.
So now we have copied the download link.
Let us minimize this.
Go to the terminal here and I'm going to navigate to desktop.
OK, it's very important to navigate through desktop because we're going to download on desktop right
here.
OK, the command to download anything is called girl, and we need to pass through arguments to it.
L and O now l is foggy direction and know is in order to create the output file.
So redirection means if we try to just download using Courland output file.
Well if GitHub tries to redirect me to some blinkx before you direct me to the final destination, I
won't be able to reach the final destination because this has no direction ability.
So this is why we are including this L and now I'm going to base the link that I have copied.
Remember, I copied the link from Download here.
Now I have just tasted it here.
Now I'm going to click on Enter and we are going to start downloading this data set.
And as we can see, it is already downloading here on desktop.
This is how much percentage has elapsed so far.
And we are going to wait until all of this fifty point three mega bytes is download.
And we are good, we have downloaded this now if you just type El's.
You are going to see the files that we have, as you can see, the file that is called like this.
OK, so I'm going to just copy this.
OK, and we need to unzip this, however, well, unzipping needs an external package, so we need to
install it.
So I'm going to go pseudo hippity get installed, unzip the package is called on the.
Here we go, and we have installed on it now we're going to use that package in order to unzip this
file.
We could just do it like this, as usual.
But I want to always teach you stuff on the terminal.
OK, so in order to unzip this, all we need to do is type, unzip and paste this the name of the file.
Where would it be unzipped?
It will be unzipped in the directory that you are in already.
OK, so we are in desktop.
It will be unzipped here.
So click enter.
And here we have folder here was created and we are zipping now there is a lot of images.
There is around sixty thousand image per category.
Cell phone number one we have thousand.
Number two three up to nine.
Each one has 60000 images.
That's a lot of that's a huge dataset.
All right, here we go.
Now, if we open it and we will see that there is two folders.
One of them is called the training.
The other is called testing.
And we are going to see how important is that because we have already learned about training and testing
and splitting our data.
OK, so the data has already split.
If I go to, let's say, to number zero, if I open any of the images and just start exploring them.
As you can see, I have a lot of handwritten digits of number zero.
Now, if I let's say go to number eight here, open any of the files again, I have a lot of files that
are about number eight.
That's great.
Now we have our dataset.
Next, we're going to continue and we're going to continue with a little bit of.