Thanks for tuning in again and welcome back to Deep Learning. In this video we want to talk about
the organizational matters, if you want to obtain the certificate, and we want to conclude the
introduction. So looking forward to a couple of exciting minutes on the topic of Deep Learning.
So let's have a look at the organizational matters. Now the module consists of five ECTS.
This is the lecture plus exercises. So it's not just sufficient to watch all of those videos.
You have to pass the exercises. In the exercises you will implement everything that we are talking
about here also in Python. We will start from scratch. So we will need to implement perceptrons,
neural networks, back propagation up to Deep Learning. In the very end we will even move
ahead towards GPUs and also large Deep Learning frameworks. So this is a mandatory part and it's
not just sufficient to pass the written exam. The content of the exercise is Python. You will do
an introduction to Python if you have never used it because Python is one of the main languages
that are used in Deep Learning implementations today. 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 adjust the weights
such that they have specific properties and you will see how you can be overfitting with those
regularization techniques. Of course you will also implement recurrent neural networks.
Later we use the Python framework and also go for large-scale classification. So for the exercises
you should bring basic knowledge of Python and NumPy. You should know about linear algebra and
very important things. Image processing is a definite plus. You should know how to process
images and of course requirements for this class are actually pattern recognition fundamentals and
you should have attended other lectures of our lab already. If you haven't you might want to consult
additional references to follow this class. We are also providing the recordings of the other classes
starting from this semester by the way. So you should bring passion for coding and you will have
to code quite a bit but you can also learn quite a bit about coding in Python during the exercises.
If you haven't done a lot of programming before this class you might need quite a bit of time in
the exercises but if you complete the exercises you will also be able to implement things in deep
learning frameworks and I think this is a very good training. So it's not just theory but you will
also do all these things practically. After this course you can not just download code from Github
and run it on your own data but you will also understand the inner workings of 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're not well used to programming it will cost some additional
time. There will be five exercises throughout the semester. The unit tests for all the exercises
except the last one. In the last exercise there will be a PyTorch implementation and you will be
facing a challenge. You have to solve image recognition tasks in order to pass the exercise.
Deadlines are announced on the respective exercise sessions but you will have to register for them
in our online platform on StoodOn. In order to participate in the exercises you have to be an
enrolled student of the Friedrich-Alexander-Universität at Langen-Nürnberg. So this will not be accessible to
everybody who is watching these videos on different other sources. We're sorry about that but we
provide some open source training assignments and I will put the link into the description of this video.
So what we've seen in the lecture so far is that deep learning is more and more present in daily
life. It's not just a technique that's done in research. We've seen this emerging really into
many many different applications ranging from speech recognition, image processing and so on
up to autonomous driving. It's a very active area of research. If you're doing this lecture
you have a very good preparation for research projects with our lab but also for industry
and other partners. So typically students who graduated from this class are very popular with
industry partners, with research partners and so on. So if you manage to pass this class you will
also see that the name deep learning will appear in your transcript of records and you will see
that there is considerable interest in your skills. Okay so far we looked into the perceptron and its
relation to biological neurons. Next time on deep learning we will actually start with the next
lecture block which means that we will extend the perceptron to a universal function approximator.
Presenters
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2020-10-04
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