Studiengänge
| Abschluss |
Studiengang |
Semester |
Leistungspunkte |
| Bachelor |
Medieninformatik (B.Sc.), PV 29
|
-
|
15
|
| Master |
Computer Science and Media (M.Sc.), PV 11
|
-
|
15
|
| Bachelor |
Medieninformatik (B.Sc.), PV 11
|
-
|
15
|
| Master |
Human-Computer Interaction (M.Sc.), PV14
|
-
|
15
|
| Bachelor |
Medieninformatik (B.Sc.), PV 17
|
-
|
15
|
| Bachelor |
Medieninformatik (B.Sc.), PV 16
|
-
|
15
|
| Master |
Human-Computer Interaction (M.Sc.), PV17
|
-
|
15
|
| Master |
Digital Engineering (M.Sc.), PV 17
|
-
|
12
|
| Master |
Human-Computer Interaction (M.Sc.), PV15
|
-
|
15
|
| Master |
Computer Science for Digital Media (M.Sc.), PV 18
|
-
|
15
|
| Master |
Digital Engineering (M.Sc.), PV 19
|
-
|
12
|
| Master |
Human-Computer Interaction (M.Sc.), PV19
|
-
|
12/18
|
| Master |
Computer Science for Digital Media (M.Sc.), PV 2020
|
-
|
12
|
| Master |
Computer Science for Digital Media (M.Sc.), PV 17
|
-
|
15
|
Inhalt
| Beschreibung |
During this practice-oriented Deep Learning project, we will implement current state-of-the-art models for solving difficult tasks in the field of computer vision. During the course of the project the participants will learn how to implement and adapt models for image classification, segmentation, etc to varying problem domains. The landscape of data driven approaches is rapidly changing and researchers need a good understanding of the required tools, publicly available datasets and methods. The students will learn the design and evaluation of existing models, and how to leverage these skills to adapt and implement own models. |
| Literatur |
Supplementary Material
Datacamp Python/Shell ( free for course participants https://www.datacamp.com/groups/education )
Udacity PyTorch Intro ( free course https://www.udacity.com/course/deep-learning-pytorch--ud188 )
Deep Learning Specialisation ( free course https://www.coursera.org/specializations/deep-learning ) |
| Bemerkung |
Mandatory technology stack (no other framework allowed):
- Python
- PyTorch |
| Voraussetzungen |
● Successful completion of the course "Image Analysis and Object Recognition"
● Good programming skills in Python |
| Leistungsnachweis |
Active participation, presentations and project documentation (e.g. commented repositories) |
| Zielgruppe |
M.Sc. Comptuer Science for Digital Media / Computer Science and Media
M.Sc. Human-Computer Interaction
M.Sc. Digital Engineering
B.Sc. Medieninformatik / Informatik
offen für interessierte Bachelor- und Masterstudierende der Fakultät Bauingenieurwesen |
