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

Daniel Graupe

ISBN 10: 9811201226 / ISBN 13: 9789811201226

Editore: World Scientific Publishing Co Pte Ltd, SG, 2019

Riguardo questo articolo

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.

Codice articolo LU-9789811201226

Dati bibliografici

• Titolo: Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)
• Editore: World Scientific Publishing Co Pte Ltd, SG
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 9811201226
• ISBN 13: 9789811201226
• Rilegatura: Hardback
• Edizione: 4th.
• Condizione: New

Descrizione articolo

Riassunto

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.