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Advanced Optical Technologies (Master of Science) >>
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Deep Learning (DL)- Lecturer
- Prof. Dr.-Ing. habil. Andreas Maier
- Details
- Vorlesung
Online/Präsenz
2 cred.h, ECTS studies, ECTS credits: 2,5
nur Fachstudium, für FAU Scientia Gaststudierende zugelassen, Sprache Englisch, Information regarding the online teaching will be added to the studon course
Time and place: Fri 10:15 - 11:45, H4
- Fields of study
- 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
PF ASC-MA 2
PF DS-MA ab 1
- Prerequisites / Organisational information
- The following lectures are recommended:
https://www.studon.fau.de/crs4449450.html
- Contents
- 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.
- Recommended literature
- 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 information:
- Credits: 2,5
- Additional information
- Keywords: deep learning; machine learning
Expected participants: 41, Maximale Teilnehmerzahl: 450
www: https://www.studon.fau.de/crs4449450.html
Registration is required for this lecture.
Die Registration via: StudOn
- Assigned lectures
- UE ([online]):Deep Learning Exercises
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Lecturers: Leonhard Rist, M. Sc., Zijin Yang, M. Sc., Alexander Barnhill, M. Sc., Noah Maul, M. Sc.
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2022:
- Deep Learning (DL)
- Department: Chair of Computer Science 5 (Pattern Recognition)
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