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 lair.
Then on top of that crucial Lehre we applied the reglue or rectified linear unit to remove any clarity
or increase non-linearity in our images.
Then we applied a pooling lair to our convolutional lair.
So from every single feature map we created a puled feature map and basically the pulling Lehre has
lots of advantages.
The main purpose of the pulling Lair is to make sure that we have a special spatial invariants 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 pulling significantly reduces the size of our images.
Also pooling helps with avoiding any kind of overfitting of our data or overall 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're after just because the way instruction
and the pooling were used was Max pooling.
Then we flattened all of the pooled images into one long vector or column of all of these values and
we put that into an artificial neural network and that was step flattening.
And Step four is a fully connected artificial neural network where all of these features are processed
through a network and then we have this final Lehre 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.
Lots of lots of iterations and at parks 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 neurons work part but also
the feature detectors are trained and adjusted in that same ingredient 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 L.D. to disband from 2016.
You can see the link up 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 CNN's that have been created by
people like you and licken 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 are 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.
There can be illusional 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 are not an exception.
They are like an architecture challenge you have to come up with a idea and then structure 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 see you next time.
Until then enjoy deep learning.