£138.12

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De Gruyter Neural Networks and Numerical Analysis - De Gruyter Series

Amazon

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Description

This comprehensive book investigates machine learning and neural networks through the lens of numerical analysis, providing rigorous mathematical foundations for understanding deep learning. The text examines how neural network representations efficiently approximate general and polynomial functions, with detailed coverage of the Latin hypercube method and the ADAM optimisation algorithm for training. The author includes practical TensorFlow implementation sessions alongside theoretical analysis, offering both foundational concepts and insights into cutting-edge research on optimised numerical schemes. Ideal for researchers, engineers and advanced students seeking to understand the mathematical principles underlying neural networks and their computational efficiency.

Key Features

The book investigates neural network methods using numerical analysis as the primary analytical tool for machine learning applications.

Detailed study of neural network representation efficiency for both general functions and polynomial functions with mathematical rigour.

Covers the Latin hypercube method and ADAM algorithm, providing original descriptions of these essential training techniques.

Includes TensorFlow implementation sessions demonstrating practical application of theoretical concepts in real machine learning projects.

Explores ongoing research into construction of optimised numerical schemes for improved neural network performance and computational efficiency.

Product Specifications

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

Barcode

No barcode data available

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