Affiliation:
1. Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati, OH 45221-0070, USA
Abstract
Artificial neural network (NN) has become one of the most widely used machine learning (ML) models for problems in science and engineering, including the fast-developing artificial intelligence (AI) technology. In training an NN model for a problem, one of the most frequently asked questions is how many neurons or layers of neurons should be used for a given dataset with a number of samples (or data points). This paper provides an answer to this critical question, by presenting a Neurons-Samples Theorem, which states, in short, that the number of neurons should be equal or less than the number of samples used to train the NN.
Publisher
World Scientific Pub Co Pte Ltd
Subject
Computational Mathematics,Computer Science (miscellaneous)
Cited by
1 articles.
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