Module Number

INFO-4365 		Module Title

Deep Convolutional Neural Networks 		Lecture Type(s)

Practical Course
ECTS 6
Work load
- Contact time
- Self study
Workload:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

To be announced.
Content

Using modern CUDA-based systems (PCs with Nvidia graphics processors as well as CUDA workstations with 4 GeForce Titan X graphics cards) and modern deep neural network training software, such as Caffe, CNTK or Torch, deep neural network machine learning problems for image classification, object recognition in images and object segmentation are investigated. Here, commonly available benchmark datasets such as NIST, Imageview, etc. are used, but also databases of RGB-D images (images with depth information, such as from the MS Kinect).
Objectives

The students can work out problems of programming, data preprocessing, structure selection of neural networks, training, validation and testing of deep neural networks independently in small groups. They have acquired competences in the areas of problem-solving behaviour, teamwork, time management, programming skills and presentation skills.
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Practical Course
P
o
4
6.0
wt
90
g
100
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Zell
Literature

Literatur wird zu Beginn des Praktikums bekanntgegeben bzw. im Praktikum ausgeteilt
Last offered unknown
Planned for currently not planned
Assigned Study Areas INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS