Neural Networks and Numerical Analysis

The aim of the series is to be an active forum for the dissemination 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
Rémi Abgrall, Universität Zürich, Switzerland
José Antonio Carrillo de la Plata, University of Oxford, UK
Jean-Michel Coron, Université Pierre et Marie Curie, Paris, France
Athanassios S. Fokas, Cambridge University, UK
Irene Fonseca, Carnegie Mellon University, Pittsburgh, USA
This book uses numerical analysis as the main tool to investigate methods in machine learning and A.I. The e?ciency 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 Tensor?ow for implementation on session and the application n to the construction of new optimized numerical schemes.