I. Introduction
To meet the challenges of successfully discovering knowledge and intelligently analyzing the ever-increasing amount of data available, researchers have developed increasingly complex Machine Learning (ML) approaches, algorithms and models. Such approaches attempt to relieve data scientists of the increasingly demanding tasks of preprocessing and transforming data. A typical example are Neural Networks (NNs) and Deep Learning (DL) methods, which, through representation learning, largely relieve experts of the time-consuming feature engineering and mapping of data spaces [1]. The trade-off for this type of automation is an immense increase in the complexity of the training process and the computational complexity of such algorithms, which require huge amounts of computing resources to run successfully [2].