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Seminar Deep Learning Theory & Applications (SemDL)- Lecturers
- Prof. Dr.-Ing. habil. Andreas Maier, Dipl.-Inf. Vincent Christlein, Lennart Husvogt, M. Sc., Katharina Breininger, M. Sc.
- Details
- Seminar
4 cred.h, benoteter certificate, ECTS studies, ECTS credits: 5
nur Fachstudium, Sprache Englisch
Time and place: Mon 16:00 - 18:00, 01.134
- Fields of study
- WPF INF-MA 1 (ECTS-Credits: 5)
WPF MT-MA-BDV 1 (ECTS-Credits: 5)
WPF CE-MA-TA-MT 1 (ECTS-Credits: 5)
- Contents
- Deep Neural Networks or so-called deep learning has attracted significant attention in the recent years. Interestingly the concept of Neural Networks inspired researchers already over generations since Minky's famous book (cf. http://en.wikipedia.org/wiki/Society_of_Mind ). Yet again, this technology brings researchers to the believe that Neural Networks will eventually be able to learn everything (cf. http://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn ).
In this seminar, we will investigate the basics of neural networks as already found in Minsky's design and expand on this on the advances to modern deep convolutional neural networks. In addition, we will also investigate open-source deep learning libraries and look into the newest results in literature.
- Recommended literature
- Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, Arxiv, 2012.
Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press Gradient-Based Learning Applied to Document Recognition, Yann Lecun, 1998 Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Srivastava et al. 2014 Greedy layer-wise training of deep networks, Bengio, Yoshua, et al. Advances in neural information processing systems 19 (2007): 153. Reducing the dimensionality of data with neural networks, Hinton et al. Science 313.5786 (2006): 504-507. Training Deep and Recurrent Neural Networks with Hessian-Free Optimization, James Martens and Ilya Sutskever, Neural Networks: Tricks of the Trade, 2012. Deep boltzmann machines, Hinton et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Pascal Vincent et al. A fast learning algorithm for deep belief nets, Hinton et al., 2006 ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, NIPS 2012 Regularization of Neural Networks using DropConnect, Wan et al., ICML OverFeat: Integrated recognition, localization and detection using convolutional networks. Computing Research Repository, abs/1312.6229, 2013. http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial http://deeplearning.net/tutorial/ Deep Learning Course on Coursera by Hinton DL platform with GPU support: caffe, lasagne, torch etc.
- ECTS information:
- Credits: 5
- Additional information
- Keywords: deep learning; neural networks; machine learning; pattern recognition
Expected participants: 10, Maximale Teilnehmerzahl: 10
www: https://www5.cs.fau.de/lectures/ss-16/seminar-deep-learning-theory-applications-semdl/
Registration is required for this lecture.
Die Registration via: persönlich beim Dozenten
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2016:
- Seminar Deep Learning Theory & Applications (SemDL)
- Seminar Medizintechnik und Medizinethik (Medtech Ethik)
- Department: Chair of Computer Science 5 (Pattern Recognition)
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