Artificial neural networks and deep learning

Abstract

Artificial intelligence has generated a lot of hype in the last decade as the performance possibilities of machines have improved, and therefore, more computational methods have become possible. In this article, we provide an overview of 2 popular supervised learning methods: artificial neural networks and deep learning. In traditional machine learning (ML), a specialist can derive informative features (variables) from a complex dataset. This requires a lot of domain knowledge and expertise. Deep learning methods are also able to learn from the data and derive important features without interference from a human. Another difference among other supervised learning methods mentioned in previous articles,1-3 and the deep learning methods is related to the learning process. In deep learning, features can be extracted sequentially in a layered structure. These layers are also called neural networks (NNs) or perceptrons, a reference to neurobiology.