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Hello my friends,

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and welcome to this very exciting
new practical activity

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on convolutional Neural networks.

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If you're not too overwhelmed
by the previous section, you know,

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on artificial neural networks, well,

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then you will smash with me
this new section, because basically

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a convolutional neural network,
as you saw in the intuition lectures,

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simply consists of adding an extra layer,
which is the convolutional layer that,

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as you understood, gives eyes to the eye,
you know, to the deep learning model.

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Because indeed,
with a convolutional neural network,

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we can now take an image or,
you know, a 3D frame as an input,

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as opposed to our previous
artificial neural network,

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which could only take an input vector
containing some features as information.

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Right. Remember the features
of the customers in the bank.

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But here we are going to add at the front
a convolutional layer

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which will be able to visualize
or see images just like humans do.

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So that's pretty exciting.

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You know, we're making a step further
towards human intelligence because,

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you know, the fascination
that scientists have about deep learning

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is that, you know, it's
the branch of machine learning that gets

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artificial intelligence
closer and closer to human intelligence.

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And you will even see
in some more advanced courses

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on deep learning that
not only we can add eyes to the AI,

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but also some memory with the Lstm cells,
but also some critic sense.

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Well, there are a lot of innovations

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in the deep learning science,
which is absolutely fascinating.

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But here we're going to focus on code.

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And and so there you go my friends.

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Let's start this new practical activity.

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And as usual, before we go into this
folder, let's make sure everyone here is

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on this same page.

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I give you the link to this folder
right before this tutorial in the article.

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And so now please follow me into parts
eight Deep Learning,

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where inside we're now going to go to

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Convolutional Neural Networks.

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All right.

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So for this section exceptionally
we only have an implementation in Python.

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And that's for the simple reason
that R is more useful for data mining

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kind of machine learning or,

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you know, advanced statistics,
high dimensional statistics.

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But I have not met any scientist in
my life who does computer vision with R.

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There are some libraries, like deep water,
that can build a CNN

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for computer vision,
but really the best and most advanced

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programing language
for deep learning is definitely Python.

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Thanks to the amazing libraries that have
been developed by Google and Facebook.

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You know, Google developed TensorFlow,

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which is an amazing library
for deep learning, and Facebook developed

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PyTorch, which is another amazing
deep learning library.

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But these two TensorFlow and PyTorch
are only used with Python.

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And that's why, exceptionally
for this section, there will not be an R

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implementation of the CNN, right?

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The A9 was fine

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because we were still doing some advanced
data mining with neural networks.

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But for computer vision, really
your best option is Python.

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All right. So let's go into Python.

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And now second different thing
compared to before.

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As you notice

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you will only find the implementation
here in this folder and not the data set.

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That's for very good reason.

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That's because the data set
is actually super huge.

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You know in terms of size
it's a couple hundreds of megabytes

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because the data set contains
lots and lots of images of cats and dogs.

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We're going to see them in a second.

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But since it contains a lot of images,
well, I did not include the data set here.

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Otherwise.

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You know, for those of you
who want to download

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the whole machine learning, it is
it codes and data sets folder.

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Well, it will take a lot more time
if I include this data set here,

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then if I left it separately,
which is what I did, I left it separately

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and I give it to you actually right
before this tutorial in the same article.

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So I hope you downloaded it, because
right now we're going to have a look

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right at the bottom of the article.

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Right before this tutorial
you had to download this exact folder.

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Well, actually the zip folder,
but I already unzipped it.

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But you had to download this folder.

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Section 40 Convolutional Neural Networks,
which indeed contains

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not only the codes in Ipynb format
and py format, but mostly the data set.

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The data set
which contains three subfolders,

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the first one containing
all the images of cats and dogs.

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But for the training set, which means that
we're going to train our CNN model

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on all these images of cats
and dogs in the train set.

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And then we have this other folder,
which is the test set on which

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we will actually evaluate our model
with new images

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of cats and dogs
on which the model wasn't trained.

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And finally, we have this little folder
here which only contains two images.

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And this will be just to test
the model in production.

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I know the single image.

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So we will first take this image
of this beautiful dog

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to test that our CNN can detect.

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The dog you know, can predict that indeed
we have a dog in this image and we will

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do the same with that image of a cat
so that we can check that.

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Indeed, our CNN predicts
that there is a cat in this image.

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So as you understood,
I just explained the problem.

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Actually, we're going to build and train
a convolutional neural network

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to recognize
if there is a dog or a cat in an image.

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Okay, so I hope you like the case study.

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Of course,

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this time it's not a business case study,
but we're going to have a lot of fun

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doing this.

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And it's a great way to be introduced
to convolutional neural networks.

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And even if it's just an introduction,
you will see that the CNN we're going to

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build will be quite advanced and actually
the implementation is quite advanced

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with lots of technical tools, which will
of course perfectly do the job at the end.

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Okay,
so that was the second specific thing.

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The data set was given to you separately.

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All right.

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And just to show you quickly
the training set and the test set.

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So in the training set you have lots
and lots of images of cats and dogs.

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Actually you have 4000 images of cats

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and 4000 images of dogs.

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Right. I can take anyone. This is a dog,

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another dog.

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You know, many different breeds of dogs.

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So if you love dogs,
you're going to have a great time

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looking at the images here, right.

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And there we go.

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So this is a very cute dog by the way.

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Okay. And same for the cat.

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You have many images of cats right.

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Have different style different colors
you know.

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So whether you're a cat lover or dog lover

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you will have great time
looking at any of these images.

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And same for the test set.

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Well, you have 1000 images of cats here
and same 1000 images of dogs.

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So just to recap, in the training set
you have until 8000 images

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with 4000 images of cats
and 4000 images of dogs.

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And in the test set,
you have in total 2000 images

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with 1000 images of cats
and 1000 images of dogs.

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Right?

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It's important to keep the numbers in mind
because we're going to use them

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in the implementation.

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Okay, good.

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So make sure to have this on your machine
because of course we will use it

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to run our code.

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And now the third specific thing
that I wanted to highlight in this

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first introductory tutorial,
it is the fact that

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for the very first time, we won't be able
to run our implementation,

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which is this one
convolutional neural network that ipynb.

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We won't be able to run this
implementation on Google Colab.

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Why is that?

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That's for the simple reason

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that the data set
is too big for a Google Colab notebook.

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And therefore,
for the very first time in this course,

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I will show you
how to run this implementation.

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On July Notebook, because with Jupyter
Notebook, we will be able to access

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directly from your machine the data set,
and we will run the code from our machine.

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But within a Jupyter notebook.

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Well notebook okay.

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So what we'll do in fact in
this new section is we will still,

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you know, re-implement this whole code
from scratch on Google Colab.

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But at the end, once
all this is implemented,

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we will simply go to file here
and then click here download dot Ipynb.

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And this will download the Ipynb file
which will be able to run on

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Jupyter Notebook.

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All right. So that's what will happen.

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But we will stay on Google Colab
to implement this together.

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And speaking of which, well right now
we have to save a copy in drive

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because this is in read only mode
and therefore we need to create a copy

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to be able to modify and re-implement
the whole code.

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All right.

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So let's do this as usual.

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Let's delete all the code cells right.

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Only the code cells.

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Make sure to keep the text cells
to keep that well highlighted structure.

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Because indeed
now we have a pretty long implementation.

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So it's important to keep the structure
so that we can,

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you know, take a step back
any time and know where we are going.

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All right.

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So let's remove only the code cells. And

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now reaching
part three, you know you're going

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to recognize some of the same steps
as before with the CNN.

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Right. This is part four. And there we go.

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All right.

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So that's the whole structure.
Let's have a look at it.

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You know from here.

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So at first we will import the libraries.

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Then we have a structure in four parts.

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Part one do reprocessing.

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Part two building the CNN, part
three training

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the CNN and part
four making a single prediction.

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So it's exactly the same as before,
you know, with the CNN,

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only this time in part one,
we will do a different work

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because for the very first time, actually
in the course, we won't be preprocess

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a classic data set, but this time
we will be pre-processing some images

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and therefore the data
preprocessing phase will be different.

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It will consist of doing two steps.

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First,

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we will preprocess the training set
and then we will preprocess the test set.

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All right.

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Then in part two we will build a CNN

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know the whole architecture
of the convolutional neural network.

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We will initialize the CNN
as a sequence of layers.

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Then we will proceed
to step one convolution to add

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the convolutional layers.

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Then we will proceed to step two pooling,
more specifically max pooling.

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Then we will add a second
convolutional layer to make it, you know,

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a deep neural network
as opposed to a shallow neural network.

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Then we will proceed to step three
flattening to flatten the result

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of all the convolutions
and pooling into one dimensional vector,

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which will become the input
of a fully connected neural network.

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And finally we will connect to all this,
the final output layer.

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Then in part three,
when training the CNN will same as a.

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And then we will first compile the CNN.

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And then we will train the CNN
on the training set while,

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you know, evaluating it on the test set,
we will clearly see that in the output.

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And finally,

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we will make a single prediction,
you know, to test our model in prediction.

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And so we will deploy our CNN
on two different images,

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one that will have a dog
and the other that will have a cat.

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And we will hope that our CNN
can recognize

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respectively, the dog and the cat.

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All right. So that's our structure.

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I hope you are ready for it.

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And actually, I'm sure you are.

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So if that's the case, well,

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join me in the next tutorial
to smash this implementation.

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And until then, enjoy machine learning.