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Computational Engineering (Rechnergestütztes Ingenieurwesen) (Master of Science) >>
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Deep Learning (DL)- Lecturers
- Prof. Dr.-Ing. habil. Andreas Maier, Katharina Breininger, M. Sc.
- Details
- Vorlesung
2 cred.h, ECTS studies, ECTS credits: 2,5
nur Fachstudium, Sprache Englisch
Time and place: Thu 8:30 - 10:00, H4
- Fields of study
- WPF INF-MA ab 1
WPF MT-MA-BDV 1
- Prerequisites / Organisational information
- The following lectures are recommended:
Application via https://www.studon.fau.de/crs2526786.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: 120, Maximale Teilnehmerzahl: 120
www: https://www5.cs.fau.de/lectures/ss-19/deep-learning-dl/
Registration is required for this lecture.
Die Registration via: StudOn
- Assigned lectures
- UE: Deep Learning Exercises
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Lecturers: Hendrik Schröter, M. Sc., Leonid Mill, M. Sc., Katharina Breininger, M. Sc.
www: http://www5.cs.fau.de/lectures/ss-19/deep-learning-dl/
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2019:
- Deep Learning (DL)
- Department: Chair of Computer Science 5 (Pattern Recognition)
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