|Name||Deep Learning Architektures|
|Verantwortlich||Prof. Dr. Rainer Schmidt|
Attendance: about 60 Hrs., Independent Study: about 90 Hrs.
In order to pass successfully this class, you should have basics of linear algebra, statistics, software development as well as good English language skills. The course is open to International students as well as students from other departments who qualify as stated above (Courses in English).
Learning objectives are the understanding of deep neural networks and how they apply to different application fields. The students will be able to train, optimize and enhance performance in their deep models, getting a flavor for different frameworks and practical approaches to deep learning.
After an introduction in the learning theory and in the machine learning, we are going to explore different deep learning architectures, reading and understanding recent notable papers in this area.
|Medien und Methoden||
Books, Internet (General Information), Slides, Journals, .pdfs
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016).
Schmidhuber, J.: Deep learning in neural networks: An overview. Neural Netw. 61, 85–117 (2015).
Nielsen, M.A.: Neural Networks and Deep Learning. (2015).
Abu-Mostafa, Y.S., Magdon-Ismail, M., Lin, H.-T.: Learning from data : a short course. [United States] : AMLBook.com (2012).
And selected Papers.