Deep Learning

Monday 4 - 5:45 pm // Dive inside deep neural networks to get in-depth knowledge about how and why they work!


Für diesen Kurs kann man keine Teilnahmenbescheinigung erhalten.

Bei ausreichender Teilnahme und Erbringung eines der angebotenen Leistungsnachweise kannst Du ein Leistungszertifikat erwerben.



This course is an online course via Zoom.

Our courses are open to all and free of charge for students, pupils, trainees, founders, scientific staff, job seekers, refugees, and pensioners. For employees and self-employed persons, a fee of 200 euros is charged when acquiring a certificate of achievement. Employees and self-employed persons can apply for a scholarship via

Your Background

You have knowledge about programming and linear algebra (working with vector and matrices) and are interested in getting in-depth knowledge on how to implement machine learning algorithms.
If you are interested in machine learning from a practical perspective, check out also our Machine Learning with Tensorflow course.

The Course Content

This course is based on the first three courses of the online Coursera Specialization "Deep Learning" provided by (see The instructor is Andrew Ng, co-founder of Coursera, founding lead of the Google Brain Team, and professor in computer science at Stanford University.

The course will provide you with an introduction to machine learning and deep learning, teach you the theoretical foundation behind neural network and how to implement them. You will then apply the newly aquired knowledge to implement your own machine learning project in a team.

How It Works

The course will take place every Monday from 4 pm to 5:45 pm. Before the first meeting there will be a semesterkick-off on the 05.11.2020.

During the week you will be expected to work through the assigned online course content, which will take you between 4 to 5 hours each week, open questions considering the course content and possible additional implications will then be discussed in the weekly offline course on Monday. Towards the end of the semester you will then work in a team on your own machine learning project.
All needed software and online course content is free. For the practical assignments in the online course, however, it will be necessary to create a Google account.

The Formalities

In order to receive a certificate of attendance ("Leistungszertifikat") for this course, active participation is expected, and no more than two classes may be missed. The active participation is proven via the final presentation of your project by you and your team, and the delivery of a well documented project source code. The same conditions apply in order to receive ECTS.

In the online sessions it is necessary that you always provide your full name in Zoom so that your presence is registered on the EDU platform. No mere certificate of attendance will be issued for this course.

Further details may be given in the course.

If you want to learn more about your course lead, check him out here:


Deep Learning
Wintersemester 2020/2021
16:00 - 17:45 Uhr


Luca Palmieri
Start: 05.11.2020
Ende: 25.01.2021

Short introduction about the course, the teacher, how it will work and what you can expect from it.
A round of introduction from all participants to get to know each others.

Henrik Horst

First week of the online course. Introduction about tools and support for set up.
First session with quiz and round of discussions and first homework.

Luca Palmieri

Second week of the first Coursera course. Discussion about python environment, dot product against element wise multiplication, and first exercise session training a small neural network recognizing cats!

Luca Palmieri

Backpropagation: some math to understand the basics behind the magic. Weights initialization in the training and notebook example of planar data classification changing the number of hidden unit in a shallow network - only 1 hidden layer.

Luca Palmieri

Deep neural network. First example of generalizing a neural network with L layers. Discussion and choice about the projects. Quick overview of different network architectures and when they may be used. End of the first Coursera course.

Luca Palmieri

Beginning of the secound Coursera course. Practical hints about initialization and regularization techniques to avoid overfitting and improving the training of a neural network.

Luca Palmieri

Mini-batches, Momentum, RMSProp and AdamOptimizer: an overview of optimization algorithm to train faster neural networks.

Luca Palmieri

Quick overview of the projects, discussion about batch normalization and hyperparameters search, first assignment using Tensorflow to create a small neural network. Small discussion about Tensorflow ideas and modalities and difference between 1.0 and 2.0 versions.

Luca Palmieri

Overview of the projects, discussion about how to structure the training, test and validation set, and more general on how to structure the whole project.
Walkthrough an LSTM tutorial on time series.

Luca Palmieri

Guidelines for the projects presentations, suggestions on what to put on them, baseline and human performances. What about using transfer learning, end-to-end approaches, divide the problem into smaller subproblems, or using multi-tasking? Sometimes the problem can be seen from another perspective.
Walkthrough a small code used for hyperparameter search: grid search, randomized search, and an example of tensorboard to visualize results

Luca Palmieri

Each Project will have time to make a presentation. After each presentation a round of questions will follow.
The exact schedule and time will be decided based on the number of projects.

Luca Palmieri