Machine learning and deep learning
Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-270155
- Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals ofToday, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.…
Autor(en): | Christian Janiesch, Patrick Zschech, Kai Heinrich |
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URN: | urn:nbn:de:bvb:20-opus-270155 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Wirtschaftswissenschaftliche Fakultät / Betriebswirtschaftliches Institut |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Electronic Markets |
ISSN: | 1422-8890 |
Erscheinungsjahr: | 2021 |
Band / Jahrgang: | 31 |
Heft / Ausgabe: | 3 |
Seitenangabe: | 685–695 |
Originalveröffentlichung / Quelle: | Electronic Markets 2021, 31(3):685–695. DOI: 10.1007/s12525-021-00475-2 |
DOI: | https://doi.org/10.1007/s12525-021-00475-2 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
Freie Schlagwort(e): | analytical model building; artificial intelligence; artificial neural networks; deep learning; machine learning |
Datum der Freischaltung: | 17.06.2022 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |