-: Hello and welcome back to the course on Deep Learning.
So, we've learned quite a lot in this section of the course,
let's summarize what we've talked about.
All right, so here we go.
We started with an input image
to which we applied multiple different feature detectors,
or also called filters, to create these feature maps,
and this comprises our convolution layer.
Then on top of that conventional layer we applied the RELU
or Rectified Linear Unit to remove any linearity
or increased non-linearity in our images.
Then we applied a pooling layer to our convolution layer,
so from every single feature map
we created a pooled feature map.
And basically the pooling layer has lots of advantages,
the main purpose of the pooling layer
is to make sure that we have
a special in variance in our images,
so basically if something tilts or twists
or is a bit different to the ideal scenario,
then we can still pick up that feature.
Plus, pooling significantly reduces the size of our images
and also pooling helps with avoiding
any kind of overfitting of our data
or of our model to the data,
because it just simply gets rid of a lot of that data.
But at the same time pooling preserves the main features
that we were after, just because the way it's structured
and the pooling we used was max pooling.
Then we flattened all of the pooled images
into one long a vector or column of all of these values
and we input that into an artificial neural network,
and that was step three flattening
and step four is the fully connected
artificial neural network
where all of these features are processed
through the network.
And then we have this final layer,
final fully connected layer, which performs the voting
towards the classes that we're after.
And then all of this is trained
through a forward propagation and back propagation process,
and lots of lots of iterations and (indistinct)
and in the end we have a very well defined neural network.
And another important thing is
not only the weights are trained
in artificial neural network part
but also the feature detectors are trained
and adjusted in that same gradient decent process,
and that allows us to come up with the best feature maps.
And in the end we get a fully trained
convolutional neural network
which can recognize images and classify them.
So, there we go,
that's how convolutional neural networks work.
And now you should be totally comfortable with this concept
and ready to proceed to the practical applications.
If you'd like to do some additional reading,
then there's a great blog by Adit Deshpande
from 2016, you can see the link over there at the bottom.
So, the blog is called "The Nine Deep Learning Papers
You Need to Know About Understanding CNN's Part Three".
And this blog actually gives you a short overview
of nine different CNNs that have been created by people like
Jan (indistinct) and others,
which you can then go ahead and study further.
So, there will be a lot of new things
that will be totally new to you
and that you will have to get your head around,
but just keep this blog in mind
or these nine papers in mind,
and even if you're not ready to go through them right now
maybe after the practical tutorials,
maybe after you do some additional training
in the space of deep learning,
slowly you can then reference these works
and, ideally, I think you will get a lot of value
through looking through other people's neural networks
and how they structured their convolution nets;
and that'll help you understand what are the best practices
and why people did certain things in a certain way.
And that will help you
with your architecture of neural networks,
Because neural networks and convolutional neural networks
are not an exception
they are like an architecture challenge.
You have to come up with a idea and then structure it,
and then adjust it and tweak it
to get the best possible design
and the best possible and optimal performance.
So, there we go, that's us for today.
I hope you enjoyed today's tutorial and this whole section,
and I look forward to seeing you next time.
Until then, enjoy Deep Learning.