Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)

Daniel Graupe

ISBN 10: 9811201226 / ISBN 13: 9789811201226

Edité par World Scientific Publishing Co Pte Ltd, SG, 2019

A propos de cet article

The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks - demonstrating how such case studies are designed, executed and how their results are obtained.The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

N° de réf. du vendeur LU-9789811201226

Détails bibliographiques

• Titre: Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)
• Éditeur: World Scientific Publishing Co Pte Ltd, SG
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 9811201226
• ISBN à 13 chiffres: 9789811201226
• Reliure: Hardback
• Édition: 4th.
• État de l'article: New

A propos de ce titre

Synopsis

The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks -- demonstrating how such case studies are designed, executed and how their results are obtained.The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.