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Finding Minima Algorithms

  • Ovidiu CalinEmail author
Chapter
  • 133 Downloads
Part of the Springer Series in the Data Sciences book series (SSDS)

Abstract

The learning process in supervised learning consists of tuning the network parameters (weights and biases) until a certain cost function is minimized. Since the number of parameters is quite large (they can easily be into thousands), a robust minimization algorithm is needed. This chapter presents a number of minimization algorithms of different flavors, and emphasizes their advantages and disadvantages.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Mathematics & StatisticsEastern Michigan UniversityYpsilantiUSA

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