Practical Course - Hands-on Deep Learning for Computer Vision (IN2106, IN4204)
Course 0000001491 in WS 2016/7
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 |
Vladimir Golkov Laura Leal-Taixe Responsible/Coordination: Daniel Cremers |
| Dates |
Assignment to Modules
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IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:- Further Modules from Other Disciplines
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. 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 - Long-short term neural networks The projects will deal with computer vision problems such as: - Digit recognition - Image classification - Segmentation - Scene understanding - Multiple object tracking - Depth from focus https://vision.in.tum.de/teaching/ws2016/dlpractice_ws2016 |
|---|---|
| Links | TUMonline entry |