Deep Learning - Modultafeln of TU Ilmenau
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|module properties Deep Learning in degree program Diplom Elektrotechnik und Informationstechnik 2017|
|department||Department of Computer Science and Automation|
|ID of group||2252 (Computer Graphics)|
|module leader||Prof. Dr. Patrick Mäder|
|term||winter term only|
|on-campus program (h)||45|
|exam||examination performance with multiple performances|
|details of the certificate|
Das Modul Deep Learning mit der Prüfungsnummer 220488 schließt mit folgenden Leistungen ab:
Details zum Abschluss Teilleistung 1:
Details zum Abschluss Teilleistung 2:
|alternative examination performance due to COVID-19 regulations incl. technical requirements|
|signup details for alternative examinations|
Mandatory registration for the aPl exams via the central registration system (Thoska) required, typically within the first 2-3 weeks of the semster.
For the DL21W course, registration is open till October 31st, 2021.
|maximum number of participants|
|previous knowledge and experience|
Professional competence gained through lectures and examined through written exam:
Deep learning has recently revolutionized a variety of application like speech recognition, image classification, and language translation mostly driven by large tech companies, but increasingly also small and medium-sized companies aim to apply deep learning techniques for solving an ever increasing variety of problems. This course will give you detailed insight into deep learning, introducing you to the fundamentals as well as to the latest tools and methods in this rapidly emerging field.
Deep learning thereby refers to a subset of machine learning algorithms that analyze data in succeeding stages, each operating on a different representation of the analyzed data. Specific to deep learning is the ability to automatically learn these representations rather than relying on domain expert for defining them manually.
The course will teach you the theoretical foundations of deep neural networks, which will provide you with the understanding necessary for adapting and successfully applying deep learning in your own to implement, parametrize and apply a variety of deep learning (CNNs) as well as recurrent neural networks (RNNs) and transformers for image, text, and time series analysis. You will further become familiar with advanced data science tools and in using computational resources to train and apply deep learning models.
|media of instruction and technical requirements for education and examination in case of online participation|
|literature / references|
|evaluation of teaching|