|
|
Advanced Signal Processing & Communications Engineering (Master of Science) >>
|
Deep Learning (DL)- Dozent/in
- Prof. Dr.-Ing. habil. Andreas Maier
- Angaben
- Vorlesung
Online
2 SWS, ECTS-Studium, ECTS-Credits: 2,5
nur Fachstudium, Sprache Englisch, Information regarding the online teaching will be added to the studon course
Zeit: Di 16:15 - 17:45, H4
- Studienfächer / Studienrichtungen
- WPF ME-BA-MG6 4-6
WPF INF-MA ab 1
WPF MT-MA-BDV 1
WPF ME-MA-MG6 4-6
WPF AI-MA ab 1
- Voraussetzungen / Organisatorisches
- The following lectures are recommended:
https://www.studon.fau.de/crs3729302.html
- Inhalt
- Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry.
This lecture introduces the core elements of neural networks and deep learning, it comprises:
(multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practices for training and evaluating neural networks visualization of networks and results common architectures, such as LeNet, Alexnet, VGG, GoogleNet recurrent neural networks (RNN, TBPTT, LSTM, GRU) deep reinforcement learning unsupervised learning (autoencoder, RBM, DBM, VAE) generative adversarial networks (GANs) weakly supervised learning applications of deep learning (segmentation, object detection, speech recognition, ...)
The accompanying exercises will provide a deeper understanding of the workings and architecture of neural networks.
- Empfohlene Literatur
- Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning. MIT Press, 2016
Christopher Bishop: Pattern Recognition and Machine Learning, Springer Verlag, Heidelberg, 2006 Yann LeCun, Yoshua Bengio, Geoffrey Hinton: Deep learning. Nature 521, 436–444 (28 May 2015)
- ECTS-Informationen:
- Credits: 2,5
- Zusätzliche Informationen
- Schlagwörter: deep learning; machine learning
Erwartete Teilnehmerzahl: 120, Maximale Teilnehmerzahl: 120
www: https://www.studon.fau.de/crs3729302.html
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt über: StudOn
- Zugeordnete Lehrveranstaltungen
- UE ([online]):Deep Learning Exercises
-
Dozentinnen/Dozenten: Florian Thamm, M. Sc., Zijin Yang, M. Sc., Noah Maul, M. Sc., Karthik Shetty, M. Sc.
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2021:
- Deep Learning (DL)
- Institution: Lehrstuhl für Informatik 5 (Mustererkennung)
|
 |
|
|
UnivIS ist ein Produkt der Config eG, Buckenhof |
|
|