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Einrichtungen >> Philosophische Fakultät und Fachbereich Theologie (Phil) >> Department Germanistik und Komparatistik >> Lehrstuhl für Korpus- und Computerlinguistik >>
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HS Deep Learning for NLP (HSprakt)- Dozent/in
- Prof. Dr. Stefan Evert
- Angaben
- Hauptseminar
2 SWS, ECTS-Studium, ECTS-Credits: 5
Bachelor
Zeit und Ort: Mi 10:15 - 11:45, 01.019
- Voraussetzungen / Organisatorisches
- Participants must register for the StudOn course linked below. Seminar places are assigned on a first come, first served basis.
- Inhalt
- Deep neural networks – also known as deep learning – have attracted significant attention in recent years. They have had a transformative influence on natural language processing (NLP) and artificial intelligence (AI), with numerous success stories and even 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.
This seminar will focus on the application of deep learning techniques to natural language processing tasks and on the topic "Social Bots: Danger or Myth?".
- Empfohlene Literatur
- 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 http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial http://deeplearning.net/tutorial/ 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-Informationen:
- Credits: 5
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 5, Maximale Teilnehmerzahl: 20
www: https://www.studon.fau.de/crs2686653.html
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt von Montag, 23.7.2018, 9:00 Uhr bis Mittwoch, 17.10.2018, 12:00 Uhr über: StudOn.
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