Patterson, J. and Gibson, A. (2017) Deep Learning: A Practitioner’s Approach. O’Reilly Media, Inc., Sebastopol, CA.

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.