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Data Science made in Switzerland

Ein Blog der ZHAW Zürcher Hochschule für Angewandte Wissenschaften

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Author: mild

Recent Posts

  • Data protection – are we really paradoxical?
  • The Psychology of Cookie Banners from a Data Privacy Perspective
  • (Neural) Networking at ANNPR 2020: International perspectives on artificial neural networks in pattern recognition at ZHAW
  • Social Media Monitoring for Arts Management on the example of the Lake of Constance (Bodensee) region
  • Too much and too irrelevant: What do users really want to know about privacy?

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    OpenAI Gym environment for Modelica models

    By Gabriel Eyyi (ZHAW) In this blog post I will show how to combine dynamic models from Modelica with reinforcement learning. As part of one of my master projects a software environment was developed to examine reinforcement learning algorithms on existing dynamic models from Modelica in order to solve control tasks. Modelica is a non-proprietary, […]

    22.11.2016 mild
    Kategorie: Allgemein, Blog, Research

    Review of SDS|2016

    By Amrita Prasad (ZHAW) It’s already been a month since we met as the Swiss Data Science community at our 3rd Swiss Conference on Data Science (SDS|2016), pushed again by ZHAW’s Datalab group and presented by SAP Switzerland. Several additional organisations sponsored and supported the conference to give it a successful execution – the organising […]

    18.10.2016 mild
    Kategorie: Allgemein, Blog, Research

    Data scientists type A & B? Nonsense.

    By Thilo Stadelmann (ZHAW) Reposted from https://dublin.zhaw.ch/~stdm/?p=350#more-350 I recently came about the notion of “type A” and “type B” data scientists. While the “type A” is basically a trained statistician that has broadened his field towards modern use cases (“data science for people”), the same is true for “type B” (B for “build”, “data science […]

    05.09.2016 mild
    Kategorie: Allgemein, Blog, Philosophy

    Deep learning for lazybones

    By Oliver Dürr (ZHAW) Reposted from http://oduerr.github.io/blog/2016/04/06/Deep-Learning_for_lazybones In this blog I explore the possibility to use a trained CNN on one image dataset (ILSVRC) as feature extractor for another image dataset (CIFAR-10). The code using TensorFlow can be found at github. Schlagwörter: Data Science, Deep Learning, Python

    26.04.2016 mild
    Kategorie: Blog, Research

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