Welcome everybody to this semester's deep learning lecture.

As you can see I'm not in the lecture hall as many as for few.

I am in my home office and we have to work from home in order to stop the current pandemic.

Therefore I decided to record these lectures and then also put them available onto the

internet such that everybody can download them freely.

You will see that we did a couple of changes to this format.

First of all we reduced the length of the lectures.

We no longer go for 90 minutes in a row.

Instead we decided to reduce the length into smaller parts such that you can watch them

in 15 to 30 minutes in one go, then stop and then continue to the next lecture.

This means that we had to introduce a couple of changes.

Of course as every semester we also updated all of the contents such that we really present

the state of the art that is up to date to current research.

Recursive self-improvement that is really the pinnacle of that where you then not only

learn how to improve on that problem and on that but you also improve the way the machine

improves and you also improve the way it improves itself.

And that was my 1987 diploma thesis which was all about that.

This first lecture will be about the introduction into deep learning.

We will deal with a broad variety of topics in this lecture.

First and foremost of course deep learning and we summarize some of the buzzwords that

you may have already heard.

We cover topics from supervised to unsupervised learning.

Of course we will talk about neural networks, feature representation and feature learning,

big data, artificial intelligence, machine learning, representation learning but also

different tasks such as classification, segmentation, regression and generation.

We are standing on the shoulders of the giants who in the past simplified the problem of

problem solving so much that now we have a chance to do the final step.

Now let's have a short look at the outline.

So first we will start with the motivation why we are interested in deep learning.

We see that we have seen tremendous progress over the last couple of years so it will be

very interesting to look into some applications and some breakthroughs that have been done.

Then in the next videos we want to talk about machine learning and pattern recognition and

how they are related to deep learning.

And of course in the first set of lectures we also want to start from the very basics.

We will talk about the perceptron and we also have to talk about a couple of organizational

matters that you will see in video number 5.

So let's look into the motivation and what are the interesting things that are happening

right now.

First and foremost I want to show you this little graph about the stock market value

of Nvidia shares and you can see here that over the last couple of years in particular

since 2016 the market value has been growing up very very much.

So one reason why this has been tremendously increasing is that approximately in 2012 the

deep learning discovery started and they really took off approximately in 2016.

So you can see that many people needed additional compute hardware.

Nvidia is manufacturing general purpose graphics processing units that allow arbitrary computation

on their boards.

In contrast to traditional hardware that doubles the compute capabilities within every two

years graphics boards double their compute power within approximately 14 to 16 months

which means that they have a quite extraordinary amount of compute power.

This enables us to train really deep networks and the state of the art machine learning