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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.
Deep Learning Neural Networks is the fastest growing field in
machine learning. It serves as a powerful computational tool for
solving prediction, decision, diagnosis, detection and decision
problems based on a well-defined computational architecture. It has
been successfully applied to a broad field of applications ranging
from computer security, speech recognition, image and video
recognition to industrial fault detection, medical diagnostics and
finance.This comprehensive textbook is the first in the new
emerging field. Numerous case studies are succinctly demonstrated
in the text. It is intended for use as a one-semester
graduate-level university text and as a textbook for research and
development establishments in industry, medicine and financial
research.
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.
Deep Learning Neural Networks is the fastest growing field in
machine learning. It serves as a powerful computational tool for
solving prediction, decision, diagnosis, detection and decision
problems based on a well-defined computational architecture. It has
been successfully applied to a broad field of applications ranging
from computer security, speech recognition, image and video
recognition to industrial fault detection, medical diagnostics and
finance.This comprehensive textbook is the first in the new
emerging field. Numerous case studies are succinctly demonstrated
in the text. It is intended for use as a one-semester
graduate-level university text and as a textbook for research and
development establishments in industry, medicine and financial
research.
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