Machine learning and deep learning

TL;DR

This paper summarizes the fundamentals of machine learning and deep learning, highlighting their differences, processes, and challenges in implementing intelligent systems across electronic markets and networked business.

Contribution

It provides a comprehensive overview of machine learning and deep learning concepts, including distinctions, processes, and implementation challenges in real-world applications.

Findings

Deep learning models often outperform traditional methods.

Implementation challenges include human-machine interaction issues.

The paper discusses AI servitization in electronic markets.

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.