Welcome to Bryton in three hours.
Hello, everyone.
This is election 12, increase our expertise and knowledge on our own by the end of this nature.
You will be able to describe how you can explore Python and its libraries online and describe how you
can teach Python to yourself.
You can always Google and search about the problem that you are looking for.
Basically, search for whatever you want to do in Python online and also look into the very first links.
Here is an example for you.
For example, we're going to open a Google search bar and then we're going to search for Contours Mapping
Library to see how we can actually implement and draw contours in Python using my library.
So I'm going to switch to a Google page and paste my search here and enter.
So if you look into these links and perhaps if you just click on the very first link, this is where
we can actually read more about my library, the people contour, which is going to actually draw for
us contours.
So if you scroll down, you see that here is the function, the variable it receives and explanations
regarding the input of that function.
And if you scroll down, they usually get some example courts.
All you can do is just copy all of these lines or simply click on the copy button here.
So everything is copied in your clipboard, then switch to your Python tab and then you can simply paste
the code here, which I already done that.
So or you basically can create a new cell and go there, which you are seeing it here.
So I just delete it.
So we first need to import the necessary libraries, which in this case, as I see in the code, decide
to use non-pay or ENPI and also the library.
And here I'm actually using PILT because I imported my plot, that pie plot class or method as.
And here we are using that control f to get our hands on that control function.
So with a little bit of changes, you'll probably be able to figure this out.
And all you have to do is just run this to see how this would look like.
Here is an example of a conter in Python.
So all we had to do is just search for it online and see if we can find some examples and read line
by line of the example and figure it out also within the site.
There are explanations regarding how we can use these different functions or what are these different
lines.
Here is another example for you guys, which is a little bit more complicated.
So we have seen the application of neural networks, but you guys might heard about convolutional neural
networks abbreviated as CNN.
So what if we can find some Tenzer?
Follow CNN examples.
What if I just search this Tenzer for cross CNN example and then copy selection here?
Switch that up and let's go back to our Google search.
And here I'm going to actually paste a search here, tens of across CNN example enter.
And we have a bunch of examples here.
I'll look into some of them, but I found this one at Victor, you dot com, a very nice and neat example.
Let's click on it.
And here are the explanation is probably a personal side of this person.
You can read the explanations here, which seems to be very good.
So at one point you are going to see some python codes like from here.
And usually at the end there are all of the codes back to back for you guys to just copy and use it
in your notebook.
So I'm going to scroll down and here is the full quote.
I always recommend you to redecide.
And when you get here, you can simply copy this full code, just select everything year after year,
copy and then switch back to your notebook, open a new cell and paste it here.
All you have to do is run this.
And if you run into any error, you can read the error message and figure out how to solve that problem.
So let's just run this one.
So let's go down.
We have an error because there is an import amnestic and the error is module not found error, no module
name amnestic.
So it doesn't actually see the Emmys.
So amnios wasn't originally installed in our Google Kulab system.
So what we can do is just open a new cell and then say, for example, install Emmies and these people
install is something that you can actually install new packages and libraries that are not in your Google
callup systems.
We know that non-pay and Ponder's and Maspero libraries or tenso are all in the Google callup system
and we just import them.
And easily use them, but there are some libraries that might not have been installed originally on
the Google crap system like Emily's, which you can install if you put a exclamation mark at the beginning
of this, basically your cell becomes like a Linux come online and using pipping style, you can install
the package that comes after, like preinstalled, amnesty.
If I run the cell, I expect that the money is being downloaded and installed successfully in our Google
collab system.
But not that if I restart my Google Kulab, I have to install it again so I don't have to do here is
just run this code.
So I'm going to run this and see if we are hitting more errors.
It seems that it is running and as you see that it probably gets some images from the administrator
and it's probably turn it into training and testing, do some data processing here.
Why they divide it by two hundred and fifty five.
We can go back to the website, go up and see why they are doing that, what was the original data?
And they now need to divide everything by two hundred and fifty five.
And what are these different convolution layers they are talking about that might be very new to you
guys.
You can always read the web like and educate yourself, but here is an easy example of these endless
data.
And as you see that we have a training procedure here which things are being trained and learned and
we are getting some kind of losses and accuracies and you can educate yourself and study these different
lines of code.
And by now, I believe that, you know enough of Python to start doing that and learn more and more
about different things.
I think this has been done.
Then you can see that our training had accuracy in a scale of 95 percent or 96 percent for validation
or testing data.
So I had I think in these codes are exactly the same codes that they had up for you.
And also here I had to install this for you guys just in case you want to use it and have these lab
notebook for yourself so you don't have to go back to the website to try this.
But I can easily delete these extra cells.
And basically you have these lines of codes here.
So this is how you can educate yourself in Python and teach Python to also just try to find a way to
express your question in a short sentence, Google it and then look into the links that are popping
up.
And then these are leads.
You follow the leads.
You learn more and more about this beautiful python libraries and educate yourself to solve the problem
that you are hitting.
In this lecture, you'll learn about how to explore Python and its libraries online and how to teach
Python to yourself.
Well, congratulations.
You are now a python.
I hope you guys enjoy discourse.
And with this short amount of time you learn enough to be able to start your journey in Python.
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for us, then we can actually go back to different lectures and modify it and make it even better.
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Thank you.
And have a wonderful python coding type.