Deep Learning is currently a big & growing trend in data analysis and prediction - and the main fuel of a new era of AI. Google, Facebook and others have shown tremendous success in pushing image, object & speech recognition to the next level.
But Deep Learning can also be used for so many other things! The list of application domains is literally endless.
Although rooted in Neural Network research already in the 1950's, the current trend in Deep Learning is unstoppable, and new approaches and improvements are presented almost every month.
We would like to meet and discuss the latest trends in Deep Learning, Neural Networks and Machine Learning, and reflect the latest developments, both in industry and in research.
The Vienna Deep Learning Meetup is positioned at the cross-over of research to industry - having both a focus on novel methods that are published in such a fast pace, and interesting new applications in the startup and industry world. We usually have 2 speakers from either academia, startups or industry, complemented by a "latest news and hot topics" section. Occasionally we do tutorials about software frameworks and how to use Deep Learning in practice. Each evening ends with networking & discussions over drinks and snacks.
Note that this meetup has an intermediate to advanced level (we have done introductions to Deep Learning and neural networks only in the beginning, but try to repeat the most important concepts regularly).
- Meetup page: https://www.meetup.com/Vienna-Deep-Learning-Meetup/
- Youtube Channel: https://www.youtube.com/channel/UCAVBJhzHK-jleJbyYTDp8cA
- Wiki: https://github.com/vdlm/meetups/wiki
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Thomas Lidy has been a researcher in music information retrieval combined with machine learning at TU Wien from 2004 to 2017. He is now the Head of Machine Learning at Musimap, a company that uses Deep Learning to analyze styles, moods and emotions in the global music catalog, in order to empower emotion-aware recommender engines. |
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Jan Schlüter has been pursuing research on deep learning for audio processing since 2010, currently as a postdoctoral researcher at the Austrian Research Institute for Artificial Intelligence (OFAI). |
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Alexander Schindler researches audio-visual aspects of music information. He is machine learning specialist at the Digital Insight Lab of the AIT Austrian Institute of Technology and lecturer at the Technical University of Vienna. |
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Rene Donner is the Head of Machine Learning & Engineering at medical image analysis startup Contextflow. |
| # | Date | Venue | Topic | Content | Video | Photos | Meetup.com |
|---|---|---|---|---|---|---|---|
| 1 | 2016-04-07 | Sektor 5 | Deep Learning - History, Approaches, Applications | more | link | ||
| 2 | 2016-05-09 | Sektor 5 | Image Synthesis / RNNs | more | link | ||
| 3 | 2016-06-06 | Sektor 5 | Theano and Lasagne | more | link | ||
| 4 | 2016-07-07 | TU Wien | more | link | |||
| 5 | 2016-09-22 | Automic Software GmbH | Automation / GoogLeNet and CaffeJS | more | link | ||
| 6 | 2016-10-12 | Sektor 5 | Intro NNs / Text-to-Speech | more | link | ||
| 7 | 2016-12-01 | Agentur Virtual Identity | more | link | |||
| 8 | 2017-01-17 | TU Wien Informatik | more | link | |||
| 9 | 2017-02-21 | bwin.party services (Austria) GmbH | more | link | |||
| 10 | 2017-03-23 | Automic Software GmbH | more | link | |||
| 11 | 2017-05-17 | Casinos Austria Innovation Hub | Distributed Deep Learning / Sound Event Detection | more | link | ||
| 12 | 2017-06-20 | FH Technikum Wien | Microsoft CNTK & Image Object Recognition / GANs | more | link | ||
| AI | 2017-09-04 | WU Wien | AI Summit Vienna 2017 | more | videos | link | |
| 13 | 2017-10-24 | Marx Palast | Google Tensorflow | more | Youtube | link | |
| 14 | 2017-11-20 | A1 Telekom Austria | Image Search | more | link | ||
| 15 | 2018-01-09 | weXelerate | Transfer Learning / Visual Computing | more | link | ||
| 16 | 2018-02-27 | A1 Telekom Austria | Word Embedding / NLP | more | Youtube | link | |
| 17 | 2018-04-23 | Wien Energie Kundendienstzentrum | Visual Computing | more | Youtube: part1 part2 | link | |
| 18 | 2018-05-07 | TU Wien | Ethics & Bias in AI | more | Youtube | link | |
| 19 | 2018-06-07 | A1 Telekom Austria | Visual Computing | more | photos | link | |
| 20 | 2018-09-18 | WKO Aussenwirtschaft Austria | Reinforcement Learning | more | (tbd) | link | |
| 21 | 2018-10-15 | Marx Palast | Music & Audio | more | link | ||
| 22 | 2018-11-12 | FH Technikum Wien | Video Surveillance / AI strategies in the government | more | photos | link | |
| WA | 2018-12-04/05 | Hofburg Wien | WeAreDevelopers AI Congress | more | link | ||
| 23 | 2019-01-31 | FH Technikum Wien | Explainable Deep Learning / NeurIPS Report | more | link | ||
| 24 | 2019-02-28 | T-Center Vienna | Ophthalmology / Computer Vision | more | link | ||
| 25 | 2019-03-27 | A1 Telekom Austria | NLP | more | link | ||
| 26 | 2019-04-29 | WKO Aussenwirtschaft Austria | Putting DL in Production | more | link | ||
| 27 | 2019-05-22 | Bosch Wien | DL in Industry | more | link |
| Date | MU# | Speaker | Topic | Slides |
|---|---|---|---|---|
| 2016-04-07 | 1 | Thomas Lidy & Jan Schlüter | Deep Learning: History, Approaches, Applications | |
| 2016-05-09 | 2 | Alex Champandard | Neural Networks for Image Synthesis | |
| 2016-05-09 | 2 | Gregor Mitscha-Baude | Recurrent Neural Networks | |
| 2016-06-06 | 3 | Jan Schlüter | Open-source Deep Learning with Theano and Lasagne | |
| 2016-09-22 | 5 | Josef Puchinger | Deep Learning & The Future of Automation | |
| 2016-09-22 | 5 | Christoph Körner | Going Deeper with GoogLeNet and CaffeJS | |
| 2016-10-12 | 6 | Benjamin Freundorfer | An Intro to Neural Networks | |
| 2016-10-12 | 6 | Kornél Kis | Deep learning in practice - a Text-to-Speech system built with neural networks | |
| 2016-12-01 | 7 | Sabria Lagoun | How can we learn from Neuroscience? | |
| 2016-12-01 | 7 | Kornél Kis | Convolutional Neural Networks: Applications and a short timeline | |
| 2017-01-17 | 8 | Thomas Lidy | Deep Learning Tutorial in Python with Keras | Github |
| 2017-02-21 | 9 | Philipp Omenitsch | Visionlabs: Face Recognition for Businesses | |
| 2017-02-21 | 9 | Alexander Schindler | Coding in Keras: Hard-Disk Failure Prediction with SMART data using RNNs | |
| 2017-03-23 | 10 | Oleg Leizerov | Deep Learning for Self-Driving Cars | |
| 2017-05-17 | 11 | Peter Ruch | A Comparison of Deep Learning Frameworks for Distributed Training | |
| 2017-05-17 | 11 | Ana Jalali | An Introduction to Bidirectional LSTM-HMM for Sound Event Detection | |
| 2017-06-20 | 12 | Philipp Kranen | Microsoft Cognitive Toolkit and Applications in Image Object Recognition | |
| 2017-06-20 | 12 | Michal Šustr | Generative Adversarial Networks | |
| 2017-09-04 | AI | Sepp Hochreiter | Deep Learning is Evolving into the Key Technology of Artificial Intelligence | |
| 2017-09-04 | AI | Tomáš Mikolov | Neural Networks for Natural Language Processing | |
| 2017-09-04 | AI | Dave Elliott | Machine Learning with Google Cloud | |
| 2017-09-04 | AI | Calvin Seward | Deep Learning: More Than Classification | |
| 2017-09-04 | AI | Ulla Kruhse-Lehtonen | Seizing the Machine Learning Opportunity | |
| 2017-10-24 | 13 | Yufeng Guo | TensorFlow Wide & Deep: Data Classification the easy way | |
| 2017-10-24 | 13 | Valentyn Boreiko | One Model To Learn Them All | |
| 2017-11-20 | 14 | Lukáš Vrabel | Evolution of Image Search @ Seznam.cz | |
| 2018-01-09 | 15 | Alexander Hirner | Transfer Learning for fun and profit | |
| 2018-01-09 | 15 | Rene Donner | Deep Learning on 3D Medical Image Data at Contextflow | |
| 2018-02-27 | 16 | Navid Rekabsaz | Demystifying Neural Word Embedding: Applications in Financial Sentiment Analysis, and Gender Bias Detection | |
| 2018-02-27 | 16 | Christoph Bonitz | Review of Andrew Ng’s Deep Learning Specialization on Coursera | |
| 2018-04-23 | 17 | Anouk Visser | Birds.ai: AI to provide a bird’s-eye view | |
| 2018-04-23 | 17 | Christoph Goetz | ImageBiopsyLab: Enhancing the medical expert - how to help doctors with AI | |
| 2018-05-07 | 18 | Moshe Vardi | Deep Learning and the Crisis of Trust in Computing | |
| 2018-05-07 | 18 | Sarah Spiekermann-Hoff | The Big Data Illusion and its Impact on Flourishing with General AI | |
| 2018-06-07 | 19 | Alexander Schindler | Visual Computing: then and now | |
| 2018-06-07 | 19 | Enes Deumić, Vedran Vekić | Fast, Accurate And Customized Visual Similarity Search On Real-world Images | |
| 2018-06-07 | 19 | Matthias Hecker | Mon Style - Machine Learning in the Fashion Domain | |
| 2018-09-18 | 20 | Eric Steinberger | Deep Reinforcement Learning: Learning Like a Baby Rather Than a Copier | |
| 2018-09-18 | 20 | Peter Ferenczy | They Grow Up So Fast | |
| 2018-10-15 | 21 | Thomas Lidy and Alexander Schindler | Deep Learning for Music & Audio Analysis | |
| 2018-10-15 | 21 | Richard Vogl | Drum Transcription via Joint Beat and Drum Modeling using Convolutional Recurrent Neural Networks | |
| 2018-11-12 | 22 | Michelangelo Fiore & Florian Matusek | Deep Learning for Object Detection in Video Surveillance | |
| 2018-11-12 | 22 | Stephanie Cox | AI Strategy for Austria | strategy paper |
| 2019-01-31 | 23 | Ahmad Haj Mosa, Fabian Schneider | Explainable Neural Symbolic Learning | |
| 2019-01-31 | 23 | Rene Donner | Interesting Papers & Trends from NeurIPS 2018 | |
| 2019-02-28 | 24 | Hrvoje Bogunovic | Deep Learning for Ophthalmology - Diagnosis and Treatment of Eye Disorders | |
| 2019-02-28 | 24 | Alexander Hirner | Computer Vision Annotation Tool | |
| 2019-03-27 | 25 | Liad Magen | An introduction to state of the art in NLP using Deep Learning | |
| 2019-03-27 | 25 | Jason Hoelscher-Obermaier | Teaching machines to understand natural language conversations: a bag of tricks | |
| 2019-04-29 | 26 | Simon Stiebellehner, Bernhard Redl | Continuous Integration and Deployment for Machine Learning Applications | |
| 2019-04-29 | 26 | Jakob Klepp | Computer Vision Models in Production |