Data Science Meetup Hamburg

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=== Doors open @ 6:30 ===

=== Networking ===

=== Small intro ===

=== Break & Networking ===

=== Talk 1 ===

Distributed Machine Learning by Tim Wirtz Senior Data Scientist @Fraunhofer IAIS

Efficient Decentralized Deep Learning by Dynamic Model Averaging

We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources. The proposed protocol allows to handle different phases of model training equally well and to quickly adapt to concept drifts. This leads to a reduction of communication by an order of magnitude compared to periodically communicating state-of-the-art approaches. Moreover, we derive a communication bound that scales well with the hardness of the serialized learning problem. The reduction in communication comes at almost no cost, as the predictive performance remains virtually unchanged. Indeed, the proposed protocol retains loss bounds of periodically averaging schemes. An extensive empirical evaluation validates major improvement of the trade-off between model performance and communication which could be beneficial for numerous decentralized learning applications, such as autonomous driving, or voice recognition and image classification on mobile phones.

=== Talk 2 ===

Challenges and Pitfalls in Attribution Modelling by Sascha Netuschil Team Lead Data Analytics bonprix.

Attribution modelling is one of the most fundamental parts of (digital) marketing. It is also a fascinating data science task with various challenges and pitfalls, many of which only become apparent once you delved deep into the data. In this talk I want to share my experiences with attribution modelling from a data science perspective and point out critical issues as well as possible approaches to solving them.

=== Networking ===

=== Closing ===