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 method, in which a parameter called learning rate plays an important role. It is in general very hard to determine its value. In this paper we generalize the preceding paper [K. Fujii: Least squares method from the view point of Deep Learning: Advances in Pure Mathematics, 8, 485-493, 2018] and give an admissible value of the learning rate, which is easily obtained.
CITATION STYLE
Fujii, K. (2018). Least Squares Method from the View Point of Deep Learning II: Generalization. Advances in Pure Mathematics, 08(09), 782–791. https://doi.org/10.4236/apm.2018.89048
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