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Neural
Smithing: Supervised Learning in Feedforward Artificial Neural
Networks |
by Russell D. Reed and Robert J. Marks
II |
ISBN: 0262181908 | |
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A practical approach to the application of multilayer
perceptron neural networks to real-world problems.
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Synopsis by Pete Loshin
How do you make a computer capable of making judgments based on
sensory inputs? Instead of building a traditional computer, try
emulating the brain. If you're comfortable with college level
calculus and statistics as well as knowledgeable about neural
networks and perceptrons, Neural Smithing can help. But don't
expect any hand-holding: Reed and Marks concentrate on practical
hints while staying clear of theoretical explanations. Despite a
lack of exercises, this book reads like a graduate level textbook.
The first chapters provide a bird's eye view of neural networks and
supervised learning, but subsequent chapters quickly zero in on
topics in supervised learning, in which systems are "adjusted" to
accelerate the learning process. Later chapters discuss specific
algorithms and methods, and many include "discussion" or "remarks"
sections, containing comments and explanations about what approaches
may be more successful than
others. |
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Back Cover |
Artificial neural networks are nonlinear mapping systems
whose structure is loosely based on principles observed in the
nervous systems of humans and animals. The basic idea is that
massive systems of simple units linked together in appropriate
ways can generate many complex and interesting behaviors. This
book focuses on the subset of feedforward artificial neural
networks called multilayer perceptions (MLP). These are the
most widely used neural networks, with applications as diverse
as finance (forecasting), manufacturing (process control), and
science (speech and image recognition). This book presents an
extensive and practical overview of almost every aspect of MLP
methodology, progressing from an initial discussion of what
MLPs are and how they might be used to an in-depth examination
of technical factors affecting performance. The book can be
used as a tool kit by readers interested in applying networks
to specific problems, yet it also presents theory and
references outlining the last ten years of MLP research.
About the Authors
Russell D. Reed is Research Assistant Professor of
Electrical Engineering, and Robert J. Marks II is Professor of
Electrical Engineering, both at the University of Washington.
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