29th Deep Learning Meetup in Vienna: Recommender Systems

Hi Deep Learners,

We are back on track after the summer. In our upcoming meetup on 24th of September we will feature Deep Learning for Recommender Systems and an overview of the fastai deep learning library:

Talk 1: Deep Learning for Recommender Systems
by Jakub Mačina, Machine Learning Engineer, Exponea

Recommender systems are driving business value through personalisation for customers of Amazon, Netflix or Spotify. This talk will provide an overview of traditional and deep learning recommender system approaches and highlight the challenges encountered by industry practitioners such as extreme data sparsity. Real-world case study will show how to capture users varying tastes and products into a dense (latent) embeddings representation in order to design a scalable recommender system architecture.

Jakub Mačina is the Machine Learning Engineer at Exponea leading a team building personalisation as a service. His research background is in the field of recommender systems with a publication at the premier conference on recommender systems research ACM RecSys. He is passionate about movement and open source software.

Talk 2: The Fastai Deep Learning Library
by Michael M. Pieler, Data Scientist

A short introduction to the PyTorch-based fastai deep learning library which covers the basic building blocks, applications, and examples.

Latest News and Hot Topics:

Jakub Mačina will give us an overview of latest hot Deep Learning approaches for Recommender Systems in his conference report from ACM Recommender Systems conference. As usual, the meetup hosts will also report on latest news in the Deep Learning domain and feature some interesting papers. If you have come across something exciting, let us know!

This meetup is supported by Raiffeisen Software GmbH which we would like to kindly thank for hosting us and providing drinks & snacks.

Looking forward to seeing you at the upcoming meetup,
Tom, Jan, Alex, René