The lecture introduces the basic concepts of deep learning, in particular different popular architectures including restricted Boltzmann machines, convolutional neural networks, stacked autoencoders (as concerns feedforward models), and long short term memory models and recursive networks (as concerns recurrent models). Different tricks for training are introduced including optimiziation schemes such as ADAM, and training modi such as drop-out, and training set enhancement. Popular applications such as for image recognition will be discussed. On a practical side,mostly PyTorchwill be used.
NOTE: this lecture will most likely take place as a compact couse at the end of September, provided the situation allows it at that time.
Programmierkenntnisse (Python oder vergleichbar), Grundlagen Mathematik, Grundlagen maschinelles Lernen (etwa Neuronale Netze oder Einführung maschinelles Lernen)
Rhythmus | Tag | Uhrzeit | Format / Ort | Zeitraum |
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Modul | Veranstaltung | Leistungen | |
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39-M-Inf-DL Deep Learning | Deep Learning | benotete Prüfungsleistung
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Studieninformation |
Die verbindlichen Modulbeschreibungen enthalten weitere Informationen, auch zu den "Leistungen" und ihren Anforderungen. Sind mehrere "Leistungsformen" möglich, entscheiden die jeweiligen Lehrenden darüber.
Students are able distinguish different key architectures for deep learning, and they know the learning scenarios where these architectures can be used. The students know how to train a deep architecture, both, as regards the algorithmic pipeline and possible forms of implementation using modern systems.
Zu dieser Veranstaltung existiert ein Lernraum im E-Learning System. Lehrende können dort Materialien zu dieser Lehrveranstaltung bereitstellen: