£131.48

De Gruyter Neural Networks and Numerical Analysis Book

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Description

Explore the intersection of machine learning and mathematical theory with Neural Networks and Numerical Analysis. This volume from the De Gruyter Series in Applied and Numerical Mathematics uses numerical analysis as a primary tool to examine methods within neural networks. Readers gain a deep understanding of how neural network representations function for both general and polynomial functions. The text provides an original look at the ADAM algorithm for training and the Latin hypercube method. To bridge theory and practice, the book includes implementation sessions using Tensorflow, making it a practical resource for those studying AI and machine learning. In addition to established methods, the book covers ongoing research regarding the construction of new optimized numerical schemes. This makes it a valuable resource for students and researchers looking to understand the mathematical foundations that drive modern artificial intelligence and computational mathematics.

Key Features

Uses numerical analysis as a core tool to investigate machine learning and neural network methods.

Provides a detailed study of neural network representations for general and polynomial functions.

Includes an original description of the Latin hypercube method and the ADAM training algorithm.

Features practical implementation sessions using the Tensorflow framework.

Covers current research focused on the construction of new optimized numerical schemes.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
05 September 2022
Listed Since
09 May 2022

Barcode

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