We will introduce the basic concepts of neural networks and some standard architectures of deep networks. We will discuss some application of deep networks in physics, including data analysis, solving ordinary differential equations and simulating statistical models. We will use python to implement deep networks within hands-on exercises.
Basic knowledge of Linear Algebra and Python are recommended but not necessary.
| Frequency | Weekday | Time | Format / Place | Period | |
|---|---|---|---|---|---|
| weekly | Mi | 16-19 | ONLINE | 11.10.2021-04.02.2022 |
| Module | Course | Requirements | |
|---|---|---|---|
| 28-M-VP Vertiefung | Vertiefung (B.1) | Graded examination
|
Student information |
| Vertiefung (B.2) | Graded examination
|
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| Vertiefung (B.3) | Graded examination
|
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| Vertiefung (B.4) | Graded examination
|
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| Vertiefung (B.5) | Graded examination
|
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| Übungen zu Vertiefung (B.1) | Study requirement
|
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| Übungen zu Vertiefung (B.2) | Study requirement
|
Student information | |
| Übungen zu Vertiefung (B.3) | Study requirement
|
Student information | |
| Übungen zu Vertiefung (B.4) | Study requirement
|
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| Übungen zu Vertiefung (B.5) | Study requirement
|
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| 28-MDP_a Methoden der Physik | Angewandte Physik | Study requirement
|
Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
This course has a video conference. Details will be displayed to you as a participant of this course. For an event with participant management, you must also be registered as attending by the teaching staff.