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 convolutional layer.
Then on top of that crucial 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 convolutional 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,
especially spatial invariance 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.
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 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 along 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 a 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 epochs.
And in the end, we have a, very well defined neural network.
And the important 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 descent 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 Aldi
to disband the, 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 CNNs part three,
and this blog actually gives you a short overview of nine different CNNs
that have been created by people like Yann LeCun 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
off to 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 convolutional nets.
And that will 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 in 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.