Worum es bei uns geht

Hamburg's largest data event. With over 1600 active members and 10 high quality events per year.

The meetup covers everything data related from data bases, data backend to data visualization, machine learning, reinforcement learning, neural nets, deep learning, ai-systems in production.

Feel free to reach out if you like to contribute in some way. This is a truly community project in Hamburg. Without you this event wouldn't be that great!

Bevorstehende Events (4)

Data Science Meetup Hamburg

jimdo

=== 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 ===

Data Science Meetup Hamburg

jimdo

=== Doors open @ 6:30 === === Networking === === Small intro === === Break & Networking === === Talk 1 === On transfer learning by Sven Giesselbach Data Scientist @ Fraunhofer IAIS === Talk 2 === Sebastian Niehaus - Data Scientist @ AICURA medical We present a reinforcement learning approach for detecting objects within an image. Our approach performs a step-wise deformation of a bounding box with the goal of tightly framing the object. It uses a hierarchical tree-like representation of predefined region candidates, which the agent can zoom in on. This reduces the number of region candidates that must be evaluated so that the agent can afford to compute new feature maps before each step to enhance detection quality. We compare an approach that is based purely on zoom actions with one that is extended by a second refinement stage to fine-tune the bounding box after each zoom step. We also improve the fitting ability by allowing for different aspect ratios of the bounding box. === Networking === === Closing ===

Data Science Meetup Hamburg

Benötigt einen Veranstaltungsort

Checkout the Event in January and February: https://www.meetup.com/Hamburg-Data-Science-Meetup/events/254907594/ https://www.meetup.com/Hamburg-Data-Science-Meetup/events/254907600/ === Doors open @ 6:30 === === Networking === === Small intro === === Break & Networking === === Talk 1 === Asma Chouchene === Talk 2 === Michael Schneider === Networking === === Closing ===

Data Science Meetup Hamburg

Benötigt einen Veranstaltungsort

Checkout the Event in February and March: https://www.meetup.com/Hamburg-Data-Science-Meetup/events/254907600/ https://www.meetup.com/Hamburg-Data-Science-Meetup/events/258130550/ === Doors open @ 6:30 === === Networking === === Small intro === === Break & Networking === === Talk 1 === RL graph a flexible computation graphs for deep reinforcement leaning by Kai Friecke @ Postdoc at Helmut-Schmidt-Universität Hamburg === Talk 2 === === Networking === === Closing ===

Vergangene Events (25)

Data Science Meetup Hamburg

Benötigt einen Veranstaltungsort

Fotos (65)