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Chapman and Hall/CRC Mathematical Foundations of Deep Learning - Theory and Algorithms

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Description

Mathematical Foundations of Deep Learning: Theory and Algorithms from Chapman and Hall/CRC provides comprehensive coverage of the mathematical principles underpinning deep learning systems. This text, part of the Chapman and Hall/CRC Mathematics and Artificial Intelligence Series, bridges the gap between abstract mathematical theory and practical algorithmic implementation. Designed for students, researchers, and professionals seeking rigorous grounding in deep learning fundamentals, the book explores theoretical frameworks and algorithmic approaches essential for understanding how modern neural networks function. Whether you are developing machine learning models, conducting research in artificial intelligence, or advancing your expertise in computational mathematics, this resource equips you with the mathematical tools and conceptual foundations necessary for working effectively with deep learning technologies.

Key Features

Chapman and Hall/CRC Mathematics and Artificial Intelligence Series title offering peer-reviewed, academic-level content on deep learning mathematics.

Combines theoretical foundations with practical algorithmic approaches to deep learning system development and implementation.

Suitable for advanced students, research scientists, and AI professionals building expertise in mathematical machine learning principles.

Covers essential mathematical frameworks for understanding neural network behaviour, training dynamics, and convergence properties.

Published by Chapman and Hall/CRC, a leading academic publisher specialising in mathematics and computational sciences.

Serves as a reference text for bridging abstract theory with real-world deep learning applications and model development.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
25 August 2026
Listed Since
04 February 2026

Barcode

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