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Machine learning and deep learning

Christian Janiesch (), Patrick Zschech () and Kai Heinrich ()
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Christian Janiesch: University of Würzburg
Patrick Zschech: Friedrich-Alexander University Erlangen-Nürnberg
Kai Heinrich: Otto-von-Guericke-Universität Magdeburg

Electronic Markets, 2021, vol. 31, issue 3, No 14, 685-695

Abstract: Abstract 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 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.

Keywords: Machine learning; Deep learning; Artificial intelligence; Artificial neural networks; Analytical model building (search for similar items in EconPapers)
JEL-codes: C6 C8 M15 O3 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12525-021-00475-2

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