Neural Networks and Numerical Analysis

[LeeAndro]

English | 2022 | ISBN: ‎ 3110783126, 978-3110783124 | 174 pages | True PDF EPUB | 22.81 MB

The series is devoted to the publication of high-level monographs and specialized graduate texts which cover the whole spectrum of applied mathematics, including its numerical aspects.​

The focus of the series is on the interplay between mathematical and numerical analysis, and also on its applications to mathematical models in the physical and life sciences.
The aim of the series is to be an active forum for the dissation of up-to-date information in the form of authoritative works that will serve the applied mathematics community as the basis for further research.
Editorial Board
Remi Abgrall, Universitat Zurich, Switzerland
Jose Antonio Carrillo de la Plata, University of Oxford, UK
Jean-Michel Coron, Universite Pierre et Marie Curie, Paris, France
Athanassios S. Fokas, Cambridge University, UK
Irene Fonseca, Carn Mellon University, Pittsburgh, USA
This book uses numerical analysis as the main tool to investigate methods in machine learning and A.I. The eciency of neural network representation on for polynomial functions is studied in detail, together with an original description of the Latin hypercube method. In addition, unique features include the use of Tensorow for implementation on session and the application n to the construction of new optimized numerical schemes.



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