Mathematics of Deep Learning
Leonid Pierre-Emmanuel Berlyand Jabin
Mathematics of Deep Learning
Mathematics of Deep Learning
Leonid Pierre-Emmanuel Berlyand Jabin
Descripción
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.
Detalles
| Formato | Tapa suave |
| Número de Páginas | 132 |
| Lenguaje | Inglés |
| Editorial | de Gruyter |
| Fecha de Publicación | 2023-04-12 |
| Dimensiones | 9.61" x 6.69" x 0.29" pulgadas |
| Serie | de Gruyter Textbook |
| Letra Grande | No |
| Con Ilustraciones | Si |
Acerca del Autor
Berlyand Jabin, Leonid Pierre
Emmanuel: - Leonid Berlyand, Pennsylvania State University, USA; Pierre-Emmanuel Jabin, Pennsylvania State University, USA.Garantía & Otros
| Garantía: | 30 dias por defectos de fabrica |
| Peso: | 0.227 kg |
| SKU: | 9783111024318 |
| Publicado en Unimart.com: | 09/11/23 |
| Feedback: |
¿Viste un precio más bajo?
Queremos saber.
|
