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Least Squares Method from the View Point of Deep Learning II: Generalization

机译:深度学习角度的最小二乘法II:泛化

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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.
机译:最小二乘法是统计中用于估计各种数据之间相关性的最基本方法之一。另一方面,深度学习是人工智能的心脏,它是一种基于最小二乘法的学习方法,其中称为学习率的参数起着重要的作用。通常很难确定其价值。在本文中,我们概括了前面的论文[K.藤井:从深度学习的角度出发,采用最小二乘法:纯数学进展, 8,485-493,2018年],并给出了可接受的学习率值,该值很容易获得。

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