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.

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.

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

First we will start with a motivation why we are interested in deep learning.

We see that we have seen tremendous progress over the last couple of years.

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.

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.

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.

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.

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 approaches.

You can see that there is a considerable dip approximately around 2019, the end of 2018,

and you can see that it's not only deep learning that is driving the market share value of Nvidia so much,

there's also another very interesting thing happening at the same time and that is Bitcoin mining.

The Bitcoin value really decreased in this period of time, also the Nvidia value went down.

So it's partially also associated to the Bitcoin.

But you can see that the value is going up again because there's a huge demand in compute power and deep learning.

Now, what are the interesting applications that we can aim at?

So the big bang of deep learning was done with the so-called ImageNet challenge.

So this is a really huge data set and this huge data set has approximately 14 million images