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Deep Learning

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Academic paper
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Academic Paper attributes

arXiv ID
1807.079870
arXiv Classification
Statistics
Statistics
0
Publication URL
arxiv.org/pdf/1807.0...87.pdf0
Publisher
ArXiv
ArXiv
0
DOI
doi.org/10.48550/ar...07.079870
Paid/Free
Free0
Academic Discipline
Statistics
Statistics
0
Machine learning
Machine learning
0
Computer science
Computer science
0
Submission Date
August 3, 2018
0
July 20, 2018
0
Author Names
Nicholas G. Polson0
Vadim O. Sokolov0
Paper 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.

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