Deep Learning
Machine Learning
2018-08-06 v2 Machine Learning
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.
Keywords
Cite
@article{arxiv.1807.07987,
title = {Deep Learning},
author = {Nicholas G. Polson and Vadim O. Sokolov},
journal= {arXiv preprint arXiv:1807.07987},
year = {2018}
}
Comments
arXiv admin note: text overlap with arXiv:1602.06561