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 (5)

Data Science Meetup Hamburg

jimdo

=== Doors open @ 6:30 === === Networking === === Small intro === === Break & Networking === === Talk 1 === Sven Giesselbach Data Scientist @ Fraunhofer IAIS with his NIPS paper on transfer learning "Corresponding Projections for Orphan Screening" We propose a novel transfer learning approach for orphan screening called corresponding projections. In orphan screening the learning task is to predict the binding affinities of compounds to an orphan protein, i.e., one for which no training data is available. The identification of compounds with high affinity is a central concern in medicine since it can be used for drug discovery and design. Given a set of prediction models for proteins with labelled training data and a similarity between the proteins, corresponding projections constructs a model for the orphan protein from them such that the similarity between models resembles the one between proteins. Under the assumption that the similarity resemblance holds, we derive an efficient algorithm for kernel methods. We empirically show that the approach outperforms the state-of-the-art in orphan screening. === 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 February: 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 === "Data hacking, from fast prototyping to production systems in order personalize Jimdo websites" by Michael Schneider Data Scientist @ Jimdo A tool stack overview on building non blocking and near real time data products in Kotlin in order to personalize websites at Jimdo. From data sourcing using apache drill to rapid prototyping, addressing the importance of machine learning evaluation, production problems and finally a/b testing with live users. Embracing the data driven culture of learning and failing fast. === 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 Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this end, we introduce RLgraph, a library for designing and executing high performance RL computation graphs in both static graph and define-by-run paradigms. The resulting implementations yield high performance across different deep learning frameworks and distributed backends. === Talk 2 === Fabian Braun Data Scientist on features beat algorithms - Improving Card Fraud Detection through Suspicious Pattern Discovery We propose a new approach to detect credit card fraud based on suspicious payment patterns. According to our hypothesis fraudsters use stolen credit card data at specific, recurring sets of shops. We exploit this behavior to identify fraudulent transactions. In a first step we show how suspicious patterns can be identified from known compromised cards. The transactions between cards and shops can be represented as a bipartite graph. We are interested in finding fully connected subgraphs containing mostly compromised cards, because such bicliques reveal suspicious payment patterns. Then we define new attributes which capture the suspiciousness of a transaction indicated by known suspicious patterns. Eventually a non-linear classifier is used to assess the predictive power gained through those new features. The new attributes lead to a significant performance improvement compared to state-of-the-art aggregated transaction features. Our results verified on real transaction data provided by our industrial partner. === Networking === === Closing ===

Data Science Meetup Hamburg

Benötigt einen Veranstaltungsort

=== Doors open @ 6:30 === === Networking === === Small intro === === Break & Networking === === Talk 1 === Andre on graph databases === Talk 2 === === Networking === === Closing ===

Vergangene Events (26)

Data Science Meetup Hamburg

jimdo

Fotos (72)