Deep Learning - CS4295
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This course will be taught during the winter term only.
Content
This course gives a fundamental introduction to the field of deep learning. It will cover most relevant techniques to understand and model recent deep learning architectures. We will start with relevant basics, computational graphs, and the concept of error backpropagation. Then will we continue with shallow networks and most important optimization strategies. Afterwards, we will study several key aspects and architectural building blocks around convolutional neural networks, but also regularization techniques and how to build very deep networks. Moreover, we will investigate important generative models and other advanced deep learning approaches of relevance.
![Deep Learning - CS4295 Deep Learning - CS4295](/fileadmin/_processed_/b/6/csm_uzl_dachfenster_01_329de5a4e0.jpg)
- Education
- Robotics - CS2500
- Project course robotics and automation - CS5295
- Artificial Intelligence I - CS3204
- Artificial Intelligence II – CS5204 T
- Deep Learning - CS4295
- Sequence Learning - CS4575
- Humanoid Robotics – RO5300
- Medical Robotics – CS4270 T
- Lab Course Robotics and Automation - CS3501
- Bachelor Project - CS3701
- Bachelor Seminar - CS3702
- Master Seminar - CS5280, CS5840
- Rescue Robotics – RO5801
- Medical Product Regulation - ME4520
- Bachelor and Master Theses
Sebastian Otte
Gebäude 64
,
Raum 96
sebastian.otte(at)uni-luebeck.de
+49 451 31015209
Coşku Can Horuz
Gebäude 64
,
Raum 89
cosku.horuz(at)uni-luebeck.de
+49 451 31015215