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Auf dem Wege zur Demokratisierung des Maschinellen Lernens

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  • Wissen - Artificial Intelligence
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Digitale Welt

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Referenzen

  1. IBM Machine Learning for z/OS, IBM Data Science Experience Local, IBM Watson Studio und IBM Db2 Analytics Accelerator

  2. Als ‘Scoren’ bezeichnet man die Anwendung des Modells auf neue Daten.

Referenzen

  1. On the Cohomology of Impossible Figures, R. Penrose, 1991, Structural Topology, 17, 11–16

    Google Scholar 

  2. Algebraic Geometry, R. Hartshorne, 1977, GTM 52, Springer

    Book  Google Scholar 

  3. Twistor Theory and Field Theory, R. Ward, R. Wells Jr, 1995, Cambridge U Press

    Google Scholar 

  4. Topological Quantum Field Theory, Nonlocal Operators, and Gapped Phases of Gauge Theories, S. Gukov, A. Kapustin, 2013, arXiv:1307, 4793v2 [hep-th]

    Google Scholar 

  5. Introduction to Topological Quantum Computing, Jannis K. Pachos, 2012, Cambridge U Press

    Book  Google Scholar 

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Authors and Affiliations

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Correspondence to Eberhard Hechler, Sandra Lemmer, Imre Koncsik, Peter Nonnenmann or Christoph Goller.

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Eberhard Hechler, Executive Architect, IBM Germany R&D Lab

Dr. Sandra Lemmer, Executive Assistant and Content Marketer, HYVE - the innovation company

Prof. Dr. Imre Koncsik, Professor für Systematische Theologie, HS Heiligenkreuz/Wien

Dr. Peter Nonnenmann, Dozent für Mathematik, DHBW Mannheim und Karlsruhe

Dr. Christoph Goller, Research Director, IntraFind Software AG

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Hechler, E., Lemmer, S., Koncsik, I. et al. Auf dem Wege zur Demokratisierung des Maschinellen Lernens. Digitale Welt 3, 64–71 (2019). https://doi.org/10.1007/s42354-019-0215-6

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  • DOI: https://doi.org/10.1007/s42354-019-0215-6

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