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Aihara, K. and Kanzaki, R. (2008) Theoretical and Engineering Approaches to Brainscience. University of Tokyo Press, Tokyo. (In Japanese)

has been cited by the following article:

• TITLE: Mathematical Reinforcement to the Minibatch of Deep Learning

  AUTHORS: Kazuyuki Fujii

  KEYWORDS: Deep Learning, Minibatch, Error (Loss) Function, Local Minima

  JOURNAL NAME: Advances in Pure Mathematics, Vol.8 No.3, March 23, 2018

  ABSTRACT: We elucidate a practical method in Deep Learning called the minibatch which is very useful to avoid local minima. The mathematical structure of this method is, however, a bit obscure. We emphasize that a certain condition, which is not explicitly stated in ordinary expositions, is essential for the minibatch method. We present a comprehensive description Deep Learning for non-experts with the mathematical reinforcement.