£108.91

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£109 today · previous high £109 · all-time low £98

Springer Machine Learning & Deep Learning in Computational Toxicology

Amazon

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Price range: £98 - £109

Price levels: 3 different prices over 38 days

Description

This Springer book compiles state-of-the-art machine learning and deep learning algorithms, methods, architectures, and software tools developed for predictive toxicology applications. The collection covers recent advances in using machine learning and deep learning techniques to analyse diverse toxicological endpoint data, providing researchers and practitioners with comprehensive coverage of modern computational approaches. The book combines informative text, figures, and tables to illustrate practical applications and methodologies, making it an essential resource for those working at the intersection of artificial intelligence and toxicology. Whether you are developing predictive models, analysing toxicological datasets, or exploring deep learning architectures for safety assessment, this volume presents both theoretical foundations and real-world implementations of machine learning solutions in toxicology.

Key Features

Springer publication offering state-of-the-art machine learning and deep learning algorithms specifically designed for toxicological applications and predictive modelling.

Comprehensive coverage of machine learning methods combined with recent deep learning techniques for analysing various types of toxicological endpoint data.

Includes practical software tools and architectural frameworks that have been successfully applied in computational toxicology research.

Features informative text, figures, and tables that clearly illustrate both theoretical concepts and real-world applications of these advanced computational methods.

Ideal reference for researchers, engineers, and practitioners developing predictive toxicology models using modern machine learning and deep learning approaches.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
08 February 2023
Listed Since
29 September 2022

Barcode

No barcode data available

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