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Chapman and Hall/CRC Mathematical Engineering of Deep Learning - Chapman & Hall/CRC

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£150.00 £107.89 £117.08 £126.27 £135.45 £144.64 £153.83 28 July 2026 14 August 2026 01 September 2026 18 September 2026 06 October 2026
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Price range: £112 - £150

Price levels: 2 different prices over 71 days

Description

Mathematical Engineering of Deep Learning delivers a comprehensive yet concise exploration of deep learning through rigorous mathematical frameworks. This Chapman and Hall/CRC Data Science Series title builds a self-contained foundation in machine learning and optimization algorithms before progressing through the essential concepts and architectures that define modern deep learning. The book systematically covers deep neural networks, convolutional models, recurrent architectures, long short-term memory networks, attention mechanisms, transformers, variational autoencoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Written for students and practitioners seeking to understand deep learning from first principles, this text bridges the gap between theoretical mathematics and practical implementation, making it an invaluable resource for engineering education and advanced study in data science and artificial intelligence.

Key Features

Chapman and Hall/CRC Data Science Series title providing mathematically rigorous coverage of deep learning theory and applications.

Self-contained introduction to machine learning and optimisation algorithms establishing foundational knowledge for advanced topics.

Comprehensive treatment of neural network architectures including convolutional models, recurrent models, and long short-term memory networks.

Coverage of transformer architectures, attention mechanisms, and modern deep learning innovations in natural language processing.

Exploration of generative models including variational autoencoders, diffusion models, and generative adversarial networks.

Integrated chapters on reinforcement learning and graph neural networks extending deep learning into specialised application domains.

Mathematical approach to deep learning engineering suitable for university courses and professional development in data science.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
03 October 2024
Listed Since
17 March 2024

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