Least Squares Method from the View Point of Deep Learning

Kazuyuki Fujii

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Least Squares Method from the View Point of Deep Learning

机译：深度学习角度的最小二乘法

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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. 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.

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的学习方法。在本文中，我们从深度学习的角度重新考虑了最小二乘法，并在非常简单的设置下彻底进行了梯度下降序列的计算。根据学习率的值（深度学习的必要参数），统计学和深度学习的最小二乘法显示出一个有趣的差异。

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《Advances in Pure Mathematics》 |2018年第5期|共9页
作者
Kazuyuki Fujii;
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中图分类数学;
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7. Least Squares Method from the View Point of Deep Learning [O] . Kazuyuki Fujii 2018

机译：来自深度学习的观点的最小二乘法

Least Squares Method from the View Point of Deep Learning

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