Mathematical Engineering of Deep Learning (Chapman & Hall/CRC Data Science Series)
by Benoit Liquet, Sarat Moka, Yoni Nazarathy
This book presents the mathematical foundations of deep learning, covering linear algebra, probability, and optimization to help readers understand neural network architectures.
About This Book
This book explores the mathematical principles underlying deep learning techniques.
It covers essential concepts from linear algebra, probability, and optimization.
Readers gain insight into how these foundations support modern neural network architectures.
The text is designed for students and practitioners seeking a rigorous understanding of the field.
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