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Deep Learning (DL)
- Lecturer
- Prof. Dr.-Ing. habil. Andreas Maier
- Details
- Vorlesung
Online 2 cred.h, ECTS studies, ECTS credits: 2,5
nur Fachstudium, Sprache Englisch, Information regarding the online teaching will be added to the studon course
Zeit: Tue 16:15 - 17:45, 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
- Prerequisites / Organisational information
- The following lectures are recommended:
https://www.studon.fau.de/crs3729302.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://www.studon.fau.de/crs3729302.html Registration is required for this lecture. Die Registration via: StudOn
- Assigned lectures
- UE ([online]):Deep Learning Exercises
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Lecturers: 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)
- Department: Chair of Computer Science 5 (Pattern Recognition)
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