Hello.
Welcome back.
In this lesson we are going to start developing a war cut or no cut neuronal network in Python.
Um one thing you would realize is when deploying machine learning on embedded devices what you wanna
do is cranial model on a computer or on a computer with a GP or in the cloud.
We use a way better devices mostly for running in frames not for training and in order to learn how
to train your own models using your own data.
I have to show you how to do it in Python so that is why we are dealing with Python now.
So we're going to see how to sort of do it our cuts or no cut neural network in Python.
And then later on we shall see how to build other neural networks using pure C quote as well.
But practically you would be using Python to build your neural network cause there is um there's so
many libraries that make it very very easy doing this type of work put a really complex neural networks
with Python.
So there'll be no reason to reinvent what exists in C language but the reason you might want to know
it from C languages as an embedded developer you think in terms of C language.
So I would use C language to explain things to you to show you how things work in C but when you are
in the field doing this work you would most probably use python to train your neural network and run
in France using C language on your embedded device.
So let's get started.
And also I would mention those of you who are already familiar with Python or familiar with building
neural networks with Python you can completely skip this but I should mention though that neural networks
were going to build in these lessons to no use in a library.
We're not going to use cameras or anything if anything just name pi is what we use we use we go according
to what we saw in the theory.
The core lesson and in the theoretical lesson when I was drawing the pseudo code I didn't show any libraries.
So we're going to execute those pure lines here.
We wouldn't use libraries later on we shall see how to use carers intensive flow.
But for now for understanding sake we need to do it raw.
Okay.
I'm going to create a new folder here.
Call this eleven
cut or not.
And in here I'm going to create two food as
accord is data set.
I stole my data sets yet and then images or test images we can store it in this photo and I'm going
to upload that data set.
I'm going to upload a data set to the video so I can download it.
The dataset we're going to use for these lessons um these initial lessons are in a format known as the
dot H5.
So these that the DOT H5 is this is image data that has been converted to dot H5.
Right.
So I'm going to put two of them here.
I have test AMG data at age 5.
Train I am t data dot age 5.
You can think of these as image data so many image data.
So many image.
Um yeah.
Image Data.
Contain any cuts and no cuts converted to this cryptic format that I'm sure some of you are not familiar
with that age 5.
So our images are here compressed and converted.
Later on we shall see how to take images.
Later on we would have.
We would go to cargo and download who download of a maybe sixty thousand images and then make them smaller
and then Crane with those images.
But is so much work and doubt divert us from the neural network.
That's why we started with this already converted image data so later on we're going to download images
and then pretty process the images on our own and then pass them through the neural network.
But for now let's use this.
So we have test AMG data that H5 train AMG data on H5.
Right.
And then um.
So um yeah.
So are we going to have this and when we have been building a new network we'll put our images here
to test a neural network with and before we start to build in the actual neural network we have to um
we have to create some helper functions to help us do things like load the image and maybe compute other
things.
So I'm going to create a python as a python file to hold the helper functions and I'm going to create
another python file to have our actual neural network.