From Machine Learning to Deep Learning: A concise introduction

This HLRS course addresses students, data scientists, and researchers who would like to have an introduction to Machine and Deep Learning methods to solve challenging and future-oriented problems. Both Machine and Deep Learning methods and examples as well as a method for data compression will be presented. Different examples are shown via hands-on sessions on an HLRS cluster (Vulcan). However, please be aware that this course is not a sequence of beginners’-to-advanced lectures about theoretical aspects of AI.

The first part will be an introduction to basic methods in Machine Learning, including pre-processing and supervised learning using Apache Spark. The course will then move on to elements of supervised Deep Learning on real data to classify annotated images of waste in the wild. Given the deluge of information needed to power machine and deep learning methods, it is imperative to think about effective data processing strategies. Therefore, the course will conclude with an introduction to data compression using the BigWhoop library. As an efficient data reduction tool, BigWhoop can be applied to generic numerical datasets to minimize I/O bottlenecks and optimize data storage. The lectures are interleaved with many hands-on sessions using Jupyter Notebooks and scripts on HLRS systems.
In addition, a guest lecture from the IAG will show how Deep Learning can be applied to problems in computational fluid dynamics.

 

Location

Online course
Organizer: HLRS, University of Stuttgart, Germany

Start date

Dec 12, 2022
08:45

End date

Dec 16, 2022
13:15

Language

English

Entry level

Basic

Course subject areas

Data in HPC / Deep Learning / Machine Learning

Topics

Artificial Intelligence

Big Data

Deep Learning

Machine Learning

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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 do not program in TensorFlow, please go over the TensorFlow tutorial (especially the "Learn and use ML" section).
Content levels

Community level: 16:30 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.

Registration closes on November 27, 2022 (extended registration phase).

Late registrations after that date are still possible according to the course capacity.

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.