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![Mathematics of Deep Learning: An Introduction](/uploads/posts/2023-04/1682703688_1431_math_matics_of_d__p_l_arning__an_introduction.jpg)
Автор: Leonid Berlyand, Pierre-Emmanuel Jabin
Издательство: De Gruyter
Год: 2023
Страниц: 134
Язык: английский
Формат: pdf (true)
Размер: 10.8 MB
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. Our goal is to introduce basic concepts from Deep Learning, e. g., mathematical definitions of DNNs, loss functions, the backpropagation algorithm, etc., in a rigorous mathematical fashion. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics.