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Practical Course - Hands-on Deep Learning for Computer Vision and Biomedicine (IN0012, IN2106, IN4204)

Course 0000003678 in WS 2017/8

General Data

Course Type practical training
Semester Weekly Hours 6 SWS
Organisational Unit Informatics 9 - Chair of Computer Vision and Artificial Intelligence (Prof. Cremers)
Lecturers Benedikt Löwenhauser
Thomas Möllenhoff
Matthias Vestner
Responsible/Coordination: Daniel Cremers
Vladimir Golkov

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks In this course, we will develop and implement deep learning algorithms for concrete applications in the field of computer vision and biomedicine. The main purpose of this course is to gain practical experience with the most successful machine learning tool in computer vision since 2012, and to learn about its benefits and drawbacks when applied to concrete, relevant problems. The topics will include: - Basics of machine learning and deep learning - Convolutional neural networks - Recurrent neural networks - tasks beyond supervised learning - Design of architectures, choice of loss functions, tuning of hyperparameters. The projects will be geared towards developing novel solutions for real open problems. Projects with different interesting problems and data representations will be offered.
Links Additional information
TUMonline entry
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