Thanks for tuning in again and welcome to deep learning. In this small video we will

look into the organizational matters and conclude the introduction. Okay, so let's look at the

organizational matters. Now the module that you can obtain here at FAU consists in total

of five ECTS and this is the lecture plus the exercises. So it's not just sufficient

to watch all of these videos. You have to pass the exercises and the exercises you will

implement everything that we're talking about here also in Python and we'll start from scratch.

So you will implement perceptrons, neural networks up to deep learning and then the

very end we will even move ahead towards GPU implementation and also large deep learning

frameworks. So this is a mandatory part. It's not sufficient only to pass the oral exam.

The content of the exercise is Python. We'll do an introduction to Python if you have never

used it because Python is one of the main languages that deep learning implementations

use today and you will really develop a neural network from scratch. There will be feed forward

neural networks, there will be convolutional neural networks. You will look into regularization

techniques, how you can actually adjust weights such that they have specific properties. You

can see how you can beat overfitting with certain regularization techniques and we will

of course also implement recurrent networks. Later we will use the PyTorch framework and

use that on large scale classification. For the exercises you should bring a basic knowledge

of Python and NumPy. You should know about linear algebra. Image processing is a definite

plus. You should know how to process images and of course requirement for this class are

actually pattern recognition fundamentals and that you have attended the other lectures

of pattern recognition already. You should bring a passion for coding. You have to code

quite a bit but you can also learn it during the exercises. If you have not done a lot

of programming before this class you will spend a lot of time with the exercises. But

if you complete those exercises you will be able to implement things in deep learning

frameworks and this is a very very good training such that you cannot just download code from

github and run it on your own data but you also understand the inner workings of the

networks, how to write your own layers and how to extend deep learning algorithms also

on a very low level. So pay attention to detail and if you are not very well used to programming

it will cost a bit of time. There will be five exercises throughout the semester. There

is unit tests for all but the last exercise. So these unit tests should help you with the

implementations and in the last exercise there will be a pytorch implementation and you will

be facing a challenge and you have to solve this image recognition task in order to pass

the exercise. Exercise deadlines are announced in the respective exercise sessions. So you

have to register to them in Stutton. And what we have seen in the lecture so far is that

deep learning is more and more present in the daily life. So it is not just a technique

that is done in research. We have seen this emerging really into many many different applications

from speech recognition, image processing and so on. Autonomous driving is something

that is going to happen probably very soon. And it is a very active area of research.

If you are doing this lecture you have a very good preparation for a research project with

our lab or industry or other partners. And we looked into the perceptron as Bernoulli

classifier and their relation to biological neurons. So next time on deep learning we

will actually start with the next lecture blog which means we will extend the perceptron

to a universal function approximator. We will look into gradient based training algorithms

for these models. And then we also look into the efficient computation of gradients. Now

if you want to prepare for the oral exam it is good to think of a couple of comprehensive

questions and questions maybe what are the six postulates of pattern recognition, what

is the perceptron objective function, can you name three applications successfully tackled

by deep learning. And of course we have a lot of further reading so you can find the

links here on the slides and we will also post the links in the description of this

video. If you have any questions you can ask them in your exercise you can email me or