Mathematics of Deep Learning

This course aims at providing a mathematical perspective to some key elements of the so-called deep neural networks (DNNs).  Much of the interest on 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.

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.

The book focuses on deep learning techniques and introduces them almost immediately. Other techniques such as regression and SVM are briefly introduced and used as a steppingstone for explaining basic ideas of deep learning.

Throughout these notes, the rigorous definitions and statements are supplemented by heuristic explanations and figures. The book is organized so that each chapter introduces a key concept.  When teaching this course, some chapters could be presented as a part of a single lecture whereas the others have more material and would take several lectures.

Easily accessible for students with no prior knowledge of deep learning and with minimal background in linear algebra and calculus. 

Focuses on the foundational mathematics of deep learning.

New chapter on kernel methods.

Additional examples.

Leonid Berland received his Ph. D.  in 1985 from Kharkiv University (Ukraine).  He joined the Pennsylvania State University (PSU) in 1991, and he is currently a Professor of Mathematics and a member of the Materials Research Institute at PSU.  He is a founding co-director of PSU Centers for Interdisciplinary Mathematics and for Mathematics of Living and Mimetic Matter.  He is known for his works at the interface between mathematics and other disciplines such as physics, materials sciences, life sciences, and most recently, computer science.  He co-authored three books and more than 100 publications. His interdisciplinary works received research awards from leading research agencies in the USA, such as NSF, the US Department of Energy, and the National Institute of Health as well as internationally (Bi-National Science Foundation and NATO). Most recently his work was recognized with the   Humboldt Research Award of 2021.   His teaching excellence was recognized by C.I. Noll Award for Excellence in Teaching by Eberly College of Science at Penn State.

Pierre-Emmanuel Jabin is currently a distinguished professor at the Pennsylvania State University since August 2020. He was a student of École Normale Supérieure from 1995 to 1999; he earned his Ph.D. in 2000 and his HRD in 2003 both at Université Pierre et Marie Curie (Paris VI). He was more recently a professor at the University of Maryland from 2011 to 2020, where he was also director of the Center for Scientific Computation and Mathematical Modeling from 2016 to 2020. Jabin‘s work in applied mathematics is internationally recognized and he has made seminal contributions to the theory and applications of many-particle/multi-agent systems together with advection and transport phenomena. Jabin was an invited speaker at the International Congress of Mathematicians in Rio de Janeiro in 2018.