![]() Theorem I and II in the text are our main results and a related problem (exercise) is presented for readers. When the learning rate changes a difference in method between Statistics and Deep Learning gives different results. Therefore, to reconsider it from the view point of Deep Learning is very natural and we carry out the calculation thoroughly of the successive approximation called gradient descent sequence. ![]() ![]() ĭeep Learning may be stated as a successive learning method based on the least squares method. On the other hand, Deep Learning is the heart of Artificial Intelligence and will become a most important field in Data Science in the near future. When we want to find properties, tendencies or correlations hidden in huge and complicated data we usually employ the method. The least squares method in Statistics plays an important role in almost all disciplines, from Natural Science to Social Science.
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