Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)
by Ovidiu Calin
Deep Learning Architectures: A Mathematical Approach explores the mathematical foundations of deep learning model design and analysis. Ovidiu Calin presents the subject within the broader context of data science, offering a rigorous resource for readers who want to understand deep learning architectures through formal mathematical ideas.
About This Book
Deep Learning Architectures: A Mathematical Approach presents deep learning through a mathematical lens.
Written by Ovidiu Calin, the book focuses on the structure and analysis of deep learning architectures.
Its approach is suited to readers seeking a more rigorous foundation for understanding models used in data science.
Published in the Springer Series in the Data Sciences, it emphasizes mathematical concepts within a contemporary computational context.
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