The goal of this book is to provide a mathematical perspective on some key elements of the so-called deep neural networks (DNNs). Much of the interest in deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying mathematical ideas. We believe that a more foundational perspective will help to answer important questions that have only received empirical answers so far. The material is based on a one-semester course Introduction to Mathematics of Deep Learning" for senior undergraduate mathematics majors and first year graduate students in mathematics. Our goal is to introduce basic concepts from deep learning in a rigorous mathematical fashion, e.g introduce mathematical definitions of deep neural networks (DNNs), loss functions, the backpropagation algorithm, etc. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics. Accessible for students with no prior knowledge of deep learning. Focuses on the foundational mathematics of deep learning. Provides quick access to key deep learning techniques. Includes relevant examples that readers can relate to easily.

Original languageEnglish (US)
Publisherde Gruyter
Number of pages126
ISBN (Electronic)9783111025551
ISBN (Print)9783111025803
StatePublished - Apr 26 2023

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics


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