Deep Neural Networks in a Mathematical Framework

Anthony L. Caterini

ISBN 10: 3319753037 / ISBN 13: 9783319753034

Verlag: Springer, Berlin, Springer International Publishing, Springer, 2018

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks.This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but alsoto those outside of the neutral network community.

Bestandsnummer des Verkäufers 9783319753034

Bibliografische Details

• Titel: Deep Neural Networks in a Mathematical Framework
• Verlag: Springer, Berlin, Springer International Publishing, Springer
• Erscheinungsjahr: 2018
• Sprache: Englisch
• ISBN-10: 3319753037
• ISBN-13: 9783319753034
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 234B x 154 H x 6 T Millimeter Breite: 234 mm, Höhe: 154 mm, Tiefe: 6 mm
• Gewicht: 184 Gramm

Über diesen Titel

Inhaltsangabe

This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks.

This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but alsoto those outside of the neutral network community.

Über die Autorin bzw. den Autor

Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.