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PRINCIPLES OF ARTIFICIAL NEURAL NETWORKS (3RD EDITION) (Advanced Series in Circuits and Systems, 7)

Daniel Graupe
4.9/5 (32102 ratings)
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Description:Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond.This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition — all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained.The book 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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with PRINCIPLES OF ARTIFICIAL NEURAL NETWORKS (3RD EDITION) (Advanced Series in Circuits and Systems, 7). To get started finding PRINCIPLES OF ARTIFICIAL NEURAL NETWORKS (3RD EDITION) (Advanced Series in Circuits and Systems, 7), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.
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PDF, EPUB & Kindle Edition
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ISBN
9814522732

PRINCIPLES OF ARTIFICIAL NEURAL NETWORKS (3RD EDITION) (Advanced Series in Circuits and Systems, 7)

Daniel Graupe
4.4/5 (1290744 ratings)
Read Now Download now ⏭ listen AudioBook
Description: Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond.This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition — all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained.The book 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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with PRINCIPLES OF ARTIFICIAL NEURAL NETWORKS (3RD EDITION) (Advanced Series in Circuits and Systems, 7). To get started finding PRINCIPLES OF ARTIFICIAL NEURAL NETWORKS (3RD EDITION) (Advanced Series in Circuits and Systems, 7), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.
Pages
—
Format
PDF, EPUB & Kindle Edition
Publisher
—
Release
—
ISBN
9814522732

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