Höchstleistungsrechenzentrum Stuttgart: From Machine Learning to Deep Learning: A concise introduction

Prerequisites and content levels

Prerequisites

Familiarity with Linux operating systems, including Linux shell (some parts of the training will use a cluster).
Access to an SSH client for remote access for the interactive portions of the training.
Technical background and basic understanding of machine learning concepts will be helpful.
Preliminary experience with Python is required. Since Python is used, the following tutorial can be used to learn the syntax.
For the third day, familiarity with TensorFlow will be a plus as all hands-on sessions will be using TensorFlow. For those who like to use TensorFlow in advance TensorFlow tutorial will be a great help.

Content levels

Basic: 10 hours 30 minutes
Intermediate: 2 hours
Community: 4 hours

Learn more about course curricula and content levels.

Learning outcomes

After this course, participants will

have a basic understanding of classical Machine Learning and Deep Learning (DL) concepts and methods,
have gained practical experience in applying these methods,
and will know how to use HLRS's systems for certain ML or DL tasks.

Instructors

Dr. Khatuna Kakhiani, Patrick Vogler and Dr.-Ing. Lorenzo Zanon (HLRS), and Anna Schwarz (IAG).

Agenda

(preliminary)

08:45 - 09:00 on every day: drop in to Zoom

Day 1 and Day 2: Focus on Pre-processing, Feature Engineering and Machine Learning (9:00 - 12:30, Dr.-Ing. Lorenzo Zanon)

The first and second day will be based on the “Stuttgart S-Bahn Example” (originally developed by Dennis Hoppe, HLRS) to provide an introduction to Machine Learning. The focus is on data preparation, classification and regression algorithms in supervised learning: Can these tools be helpful to improve the travel experience in the Stuttgart S-Bahn, which are their limits? Apache Spark will be employed for the hands-on sessions on Jupyter Notebooks as well as via interactive jobs on script. Finally, we will also touch upon the visualisation of results.

Day 3 and Day 4: Focus on data processing, Model of ANN and supervised Deep Learning to classify images of waste in the wild (9:00 - 12:00, Dr. Khatuna Kakhiani)

During these two days, participants will explore how Deep Learning can be used to classification waste in wild. After brief introduction of Deep Learning, and basic concepts and Building blocks of Deep Neural Networks, participants will learn how to:

Implement common deep learning workflow for image classification
Process data, experiment with network structure and training parameters
Deploy neural network to classify images
Visualize results Upon completion, participant will be able to solve classification problems with CNN on other custom datasets. The hands-on training using Jupyter Notebooks, interactive jobs on script and Tensorflow.

Day 5:

Guest Lecture: Towards Data-Driven Computational Fluid Dynamics (9:00 - 10:30, Anna Schwarz, IAG)
Data Compression of numerical data sets with the BigWhoop library (10:45-13:15, Patrick Vogler, HLRS)

On the fifth day we start with the guest lecture "Towards Data-Driven Computational Fluid Dynamics". It will be given by Anna Schwarz, Institute of Aerodynamics and Gas Dynamics, University of Stuttgart.

We will conclude the day with an introduction to data compression, focusing on the various methods available to us for the efficient size reduction of our training data. Special attention will be paid to which approaches are best suited for different data types and what impact the different approaches and compression rates have on the quality of the datasets. The compression library BigWhoop and its accompanying command line tool will be made available for the hands-on sessions.

Exercises

The exercises will be carried out on HLRS systems using Jupyter Notebooks.

Registration-information

Register via the button at the top of this page.
We encourage you to register to the waiting list if the course is full. Places might become available.

Fees

Students without Diploma/Master: 30 EUR
PhD students or employees at a German university or public research institute: 60 EUR
PhD students or employees at a university or public research institute in an EU, EU-associated or PRACE country other than Germany: 120 EUR.
PhD students or employees at a university or public research institute outside of EU, EU-associated or PRACE countries: 240 EUR
Other participants, e.g., from industry, other public service providers, or government: 600 EUR

Our course fee includes coffee breaks (in classroom courses only).

For lists of EU and EU-associated coutries, and PRACE countries have a look at the Horizon Europe and PRACE website.

PRACE PATC and bwHPC

HLRS is part of the Gauss Centre for Supercomputing (GCS), which is one of the six PRACE Advanced Training Centres (PATCs) that started in Feb. 2012.

HLRS is also member of the Baden-Württemberg initiative bwHPC.

This course is also provided within the framework of the bwHPC training program. This course is not part of the PATC curriculum and is not sponsored by the PATC program.

Contact

Tobias Haas, phone 0711 685 87223, tobias.haas(at)hlrs.de
Lucienne Dettki, phone 0711 685 63894, dettki(at)hlrs.de

Further courses

See the training overview and the Supercomputing Academy pages.