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Least Squares Method from the View Point of Deep Learning
International College of Arts and Sciences, Yokohama City University, Yokohama, Japan
Department of Mathematical Sciences, Shibaura Institute of Technology, Saitama, Japan
- 1 International College of Arts and Sciences, Yokohama City University, Yokohama, Japan
- 2 Department of Mathematical Sciences, Shibaura Institute of Technology, Saitama, Japan
Advances in Pure Mathematics·Volume 08 (2018)·Pages 485–493·Published 9 May 2018·DOI10.4236/apm.2018.85027
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Abstract
The least squares method is one of the most fundamental methods in Statistics to estimate correlations among various data. On the other hand, Deep Learning is the heart of Artificial Intelligence and it is a learning method based on the least squares. In this paper we reconsider the least squares method from the view point of Deep Learning and we carry out the computation thoroughly for the gradient descent sequence in a very simple setting. Depending on the values of the learning rate, an essential parameter of Deep Learning, the least squares methods of Statistics and Deep Learning reveal an interesting difference.
KeywordsLeast Squares MethodStatisticsDeep LearningLearning RateLinear Algebra
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