Deep Learning

TL;DR

Deep learning is a hierarchical machine learning technique used for high-dimensional data reduction and prediction, with widespread applications in AI, image processing, robotics, and automation, but it remains a black-box approach.

Contribution

This paper reviews the state-of-the-art deep learning methods from modeling and algorithmic perspectives, highlighting its applications and characteristics.

Findings

Deep learning effectively reduces high-dimensional data.

It is widely applied in AI, image processing, robotics, and automation.

Deep learning functions as a predictive black-box methodology.

Abstract

Deep learning (DL) is a high dimensional data reduction technique for constructing high-dimensional predictors in input-output models. DL is a form of machine learning that uses hierarchical layers of latent features. In this article, we review the state-of-the-art of deep learning from a modeling and algorithmic perspective. We provide a list of successful areas of applications in Artificial Intelligence (AI), Image Processing, Robotics and Automation. Deep learning is predictive in its nature rather then inferential and can be viewed as a black-box methodology for high-dimensional function estimation.