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

Book Details

Format Hardback or Cased Book
ISBN-10 9811201226
ISBN-13 9789811201226
Publisher World Scientific Publishing Co Pte Ltd
Imprint World Scientific Publishing Co Pte Ltd
Country of Manufacture GB
Country of Publication GB
Publication Date Apr 15th, 2019
Print length 440 Pages
Weight 996 grams
Dimensions 17.70 x 25.30 x 3.20 cms
Product Classification: Neural networks & fuzzy systems
Ksh 26,100.00
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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.

 

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.


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