Machine learning and deep learning

Abstract Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of to learn from problem-specific training data automate process analytical model building and solve associated tasks. Deep is a concept based neural networks. For many applications, deep models outperform shallow traditional analysis approaches. In this article, we summarize fundamentals generate broader understanding methodical underpinning current systems. particular, provide conceptual distinction between relevant terms concepts, explain automated through learning, discuss challenges arise when implementing such in field electronic markets networked business. These naturally go beyond technological aspects highlight issues human-machine interaction servitization.