Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)
by Ovidiu Calin
A mathematical exploration of deep learning architectures, offering theoretical foundations and analytical methods for understanding modern neural networks in data science.
About This Book
This book presents deep learning from a mathematical perspective within the Springer Series in the Data Sciences.
It explores the theoretical structures and analytical methods that underpin modern neural network architectures.
Readers gain insight into the formal frameworks used to understand and design deep learning models.
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