swamydev/meetups

Vienna Deep Learning Meetup

Slides & Resources

Overview

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).

Resources

• 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

Your Hosts

	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.
	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).
	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.
	Rene Donner is the Head of Machine Learning & Engineering at medical image analysis startup Contextflow.

Meetups

#	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

Talks & Presentations

Date	MU#	Speaker	Topic	Slides
2016-04-07	1	Thomas Lidy & Jan Schlüter	Deep Learning: History, Approaches, Applications	pdf
2016-05-09	2	Alex Champandard	Neural Networks for Image Synthesis
2016-05-09	2	Gregor Mitscha-Baude	Recurrent Neural Networks	pdf
2016-06-06	3	Jan Schlüter	Open-source Deep Learning with Theano and Lasagne	pdf
2016-09-22	5	Josef Puchinger	Deep Learning & The Future of Automation
2016-09-22	5	Christoph Körner	Going Deeper with GoogLeNet and CaffeJS	pdf
2016-10-12	6	Benjamin Freundorfer	An Intro to Neural Networks	pdf
2016-10-12	6	Kornél Kis	Deep learning in practice - a Text-to-Speech system built with neural networks	pdf
2016-12-01	7	Sabria Lagoun	How can we learn from Neuroscience?	pdf
2016-12-01	7	Kornél Kis	Convolutional Neural Networks: Applications and a short timeline
2017-01-17	8	Thomas Lidy	Full Networks, CNNs, RNNs (on images, music/speech)	Github
2017-02-21	9	Philipp Omenitsch	Visionlabs: Face Recognition for Businesses	pdf
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	google
2017-05-17	11	Peter Ruch	A Comparison of Deep Learning Frameworks for Distributed Training	pdf
2017-05-17	11	Ana Jalali	An Introduction to Bidirectional LSTM-HMM for Sound Event Detection	pdf
2017-06-20	12	Philipp Kranen	Microsoft Cognitive Toolkit and Applications in Image Object Recognition	pdf
2017-06-20	12	Michal Šustr	Generative Adversarial Networks	pdf
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	pdf
2017-10-24	13	Valentyn Boreiko	One Model To Learn Them All	pdf
2017-11-20	14	Lukáš Vrabel	Evolution of Image Search @ Seznam.cz	pdf
2018-01-09	15	Alexander Hirner	Transfer Learning for fun and profit	pdf
2018-01-09	15	Rene Donner	Deep Learning on 3D Medical Image Data at Contextflow	pdf
2018-02-27	16	Navid Rekabsaz	Demystifying Neural Word Embedding: Applications in Financial Sentiment Analysis, and Gender Bias Detection	pdf
2018-02-27	16	Christoph Bonitz	Review of Andrew Ng’s Deep Learning Specialization on Coursera	pdf
2018-04-23	17	Anouk Visser	Birds.ai: AI to provide a bird’s-eye view	pdf
2018-04-23	17	Christoph Goetz	ImageBiopsyLab: Enhancing the medical expert - how to help doctors with AI	pdf
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	pdf
2018-06-07	19	Enes Deumić, Vedran Vekić	Fast, Accurate And Customized Visual Similarity Search On Real-world Images	pdf
2018-06-07	19	Matthias Hecker	Mon Style - Machine Learning in the Fashion Domain	pdf
2018-09-18	20	Eric Steinberger	Deep Reinforcement Learning: Learning Like a Baby Rather Than a Copier	pdf
2018-09-18	20	Peter Ferenczy	They Grow Up So Fast	pdf
2018-10-15	21	Thomas Lidy and Alexander Schindler	Deep Learning for Music & Audio Analysis	pdf
2018-10-15	21	Richard Vogl	Drum Transcription via Joint Beat and Drum Modeling using Convolutional Recurrent Neural Networks	pdf
2018-11-12	22	Michelangelo Fiore & Florian Matusek	Deep Learning for Object Detection in Video Surveillance	pdf
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	pdf
2019-01-31	23	Rene Donner	Interesting Papers & Trends from NeurIPS 2018	pdf
2019-02-28	24	Hrvoje Bogunovic	Deep Learning for Ophthalmology - Diagnosis and Treatment of Eye Disorders	pdf
2019-02-28	24	Alexander Hirner	Computer Vision Annotation Tool	pdf
2019-03-27	25	Liad Magen	An introduction to state of the art in NLP using Deep Learning	pdf
2019-03-27	25	Jason Hoelscher-Obermaier	Teaching machines to understand natural language conversations: a bag of tricks	pdf