Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)
by Ovidiu Calin
A comprehensive mathematical exploration of deep learning architectures, covering theoretical foundations and frameworks for data science applications.
About This Book
Deep Learning Architectures: A Mathematical Approach presents advanced mathematical frameworks for understanding deep neural networks.
The text explores core theoretical principles underlying modern deep learning models and architectures.
Readers gain insight into the mathematical structures that support practical applications in data science.
The book is part of the Springer Series in the Data Sciences and targets graduate students and researchers.
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