|
|
Seminar Deep Learning Theory & Applications (SemDL) [Import]- Lecturers
- Prof. Dr.-Ing. habil. Andreas Maier, Prof. Dr. Stefan Evert, Dipl.-Inf. Vincent Christlein
- Details
- Seminar
4 cred.h, benoteter certificate, ECTS studies, ECTS credits: 5
nur Fachstudium, Sprache Englisch
Time and place: Wed 8:30 - 10:00, 0.154-115
- 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.
They have had a transformative influence on Natural Language Processing (NLP) and Artificial Intelligence (AI), with numerous success stories recent claims of superhuman learning performance in certain tasks.
According to Young et al. (2017), more than 70% of the papers presented at recent NLP conferences made use of deep learning techniques.
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 ).
This year we will focus on sequence learning with LSTM (long short-term memory) and similar recurrent network architectures and on their application to natural language processing tasks.
- 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 Goldberg, Yoav (2017). Neural Network Methods for Natural Language Processing. Number 37 in Synthesis Lectures on Human Language Technologies. Morgan & Claypool. Young, Tom; Hazarika, Devamanyu; Poria, Soujanya; Cambria, Erik (2017). Recent trends in deep learning based natural language processing. CoRR, abs/1708.02709. http://arxiv.org/abs/1708.02709 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. Stanford University CS 224: Deep Learning for NLP (http://cs224d.stanford.edu ) University of Oxford: Deep Natural Language Processing (https://github.com/oxford-cs-deepnlp-2017/lectures )
- ECTS information:
- Credits: 5
- Additional information
- Keywords: deep learning; neural networks; machine learning; pattern recognition; natural language processing
Expected participants: 10, Maximale Teilnehmerzahl: 10
www: https://www5.cs.fau.de/lectures/ws-1819/seminar-deep-learning-theory-applications-semdl/
Registration is required for this lecture.
Die Registration via: StudOn
- Verwendung in folgenden UnivIS-Modulen
- Startsemester WS 2018/2019:
- Seminar Deep Learning Theory & Applications (SemDL)
- Seminar Medizintechnik und Medizinethik (Medtech Ethik)
- Wahlpflichtbereich (Wahlpflicht FPO 2018)
- Wahlpflichtmodul Digitale Geistes- und Sozialwissenschaften in Theorie und Praxis (Wahlpflicht FPO 2016)
- Department: Interdisziplinäres Zentrum Digitale Geistes- und Sozialwissenschaften
|
|
|