A Study on the Relationship Between Deep Learning and Statistical Models

ABSTRACT

Recently, deep learning has become a pervasive tool in prediction problems for structured and/or unstructured big data in various areas including science and engineering. In particular, deep neural network models (i.e. a basic core model of deep learning) can be viewed as an extension of statistical models by going through the incorporation of hidden layers. In this paper, we study the relationship between both models in terms of model structures and model learning. For this purpose, we also compare the predictive performances of both models, with two practical examples.

KEYWORDS:

• Deep learning
• deep neural networks
• generalized linear model
• likelihood
• loss function

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by a Research Grant of Pukyong National University (2021).