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Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (Bradford Book)

by Russell Reed (Author), Robert J Marks II (Author)
4.4 4.4 out of 5 stars 21 ratings
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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 perceptrons (MLP). These are the mostly 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.

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  1. ISBN-10
    0262527014
  2. ISBN-13
    978-0262527019
  3. Publication date
    February 17, 1999
  4. Language
    English
  5. Dimensions
    9.25 x 7.52 x 0.75 inches
  6. Print length
    358 pages
  7. See all details
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Product details

  • Publisher ‏ : ‎ Bradford Books (February 17, 1999)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 358 pages
  • ISBN-10 ‏ : ‎ 0262527014
  • ISBN-13 ‏ : ‎ 978-0262527019
  • Reading age ‏ : ‎ 18 years and up
  • Item Weight ‏ : ‎ 13 ounces
  • Dimensions ‏ : ‎ 9.25 x 7.52 x 0.75 inches
  • Customer Reviews:
    4.4 4.4 out of 5 stars 21 ratings

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  1. Russell D. Reed

    Russell D. Reed

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  2. Robert J Marks II

    Robert J Marks II

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    Robert J. Marks II, PhD, is a Distinguished Professor at Baylor University. He is also the Director of the Walter Bradley Center for Natural & Artificial Intelligence.

    Marks is listed at TheBestSchools.com as one of the 50 most influential scientists alive today. Marks is the recipient of numerous professional awards, including a NASA Tech Brief Award and a best paper award from the American Brachytherapy Society for prostate cancer research. He is Fellow of both IEEE and Optica (formerly the Optical Society of America).

    Marks was awarded Junior Membership in the Ohio Academy of Science at the age of eighteen. He was awarded the IEEE Outstanding Branch Councilor Award, The IEEE Centennial Medal, the IEEE Neural Networks Society Meritorious Service Award, the IEEE Circuits and Systems Society Golden Jubilee Award and the IEEE CIS Chapter of the IEEE Dallas Section Volunteer of the Year award. He was was named a Distinguished Young Alumnus of Rose-Hulman Institute of Technology and is an inductee into the Texas Tech Electrical Engineering Academy, While at the University of Washington, Marks served for 17 years as the faculty advisor to the University of Washington's chapter of CRU.and is an advisor for Ratio Christi at Baylor University. He describes himself as a John 3:16 Christian.

    Marks was featured in the Ben Stein documentary Expelled: No Intelligence Allowed. The film document's the removal of Marks's web site from Baylor servers by the administration.

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Customer reviews

4.4 out of 5 stars
4.4 out of 5
21 global ratings

Top reviews from the United States

A. Kernytsky
5.0 out of 5 stars Most handled book on my bookshelf
Reviewed in the United States on May 18, 2007
Verified Purchase
After owning this book for many years and reaching for it many times, I decided that I at least owed the book a five star review.

Early in my graduate career I began working with neural networks and discovered this book in a electronic bookshelf available at my university. After printing chapter after chapter to read on subway rides home I ended up buying it for convenience. It gave me the background I needed to code up a basic artificial neural network in C++ and to then extend it to fit my needs.

The style of the writing is the perfect balance of enough detail to understand a concept or method without unnecessary wordiness. Each chapter covers an important aspect of neural network development and application - for exmaple, internode weight initilaization techniques - and acts a sort of mini-review of the most popular methods with a clear explanation of the pros and cons of each.

This is an excellent bookshelf addition for anyone who works with neural networks.
8 people found this helpful
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Sandeep Kavadi
3.0 out of 5 stars Well balanced content, but notations used are a nightmare!
Reviewed in the United States on December 13, 2020
Verified Purchase
As a beginner, I had a hard time following the content purely because of the notations. In most cases the notations used were implied rather than explicitly defined. I had a hard time going through the introductory chapters.
The book, otherwise, is pretty good. It has a good balance of theory and implementation.
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T. K. Hin
5.0 out of 5 stars provides rigor and solid math framework if one wants delve deeper into nn
Reviewed in the United States on March 29, 2019
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i needed more substance beyond the numerous web literature, which only touch the surface and minimal on rigor/math. this book was highly recommended, and i can see why from the first 2 chapters already.
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Mr.Moo
5.0 out of 5 stars This is a fantastic book. It covers a wide variety of topics ...
Reviewed in the United States on December 3, 2016
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This is a fantastic book. It covers a wide variety of topics in neural network programming and construction and breaks everything down quite nicely. It's a majorly complex subject but this book does a great job of explaining things.
2 people found this helpful
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R. Geldreich
3.0 out of 5 stars Good physical book, bad e-book
Reviewed in the United States on March 31, 2023
The e-book is chock full of formatting, equation, and figure errors. Virtually unreadable. Just get the physical book.
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Mikhail Vasilyev
5.0 out of 5 stars Comprehensive and easy to read
Reviewed in the United States on December 14, 2017
Fantastic book, it describes all aspects and details of this complex algorithm in a way which is easy to understand. It also gives a broad overview of the many techniques and algorithms that have been proposed over the last decades as enhancements of the basic feed-forward neural network architecture. Highly recommend it if you need to implement NN in your program or just want to gain a throughout understanding of the topic. It requires some knowledge of calculus though, in order to fully understand all the formulas.
One person found this helpful
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Charlie S.
5.0 out of 5 stars The most definitive guide to Neural Networks.
Reviewed in the United States on September 16, 2013
After reading a half dozen other books about neural networks, I finally stumbled on this one which pulled everything together, neatly and concisely. This is one of the few books that I continue to hall around a hard copy of.
One person found this helpful
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Brian Vandenberg
5.0 out of 5 stars Not for the weak of heart
Reviewed in the United States on January 15, 2009
I bought this 9 or 10 years ago when I became interested in AI, but my math skills were pretty weak at the time. I've since completed a CS & math degree, and I find this book easy to read, thoroughly interesting, and insightful.

Many of the questions I found myself asking while I read were soon answered as I read later sections of the book.

For those considering the purchase and unsure whether they can handle it, for much of the book a decent exposure to calculus will suffice. For a few chapters some exposure to ordinary differential equations would be wise. Numerical Analysis is probably a good idea as well.

An exposure to probability & statistics (not the freshman version) would help as well. The section on initialization techniques talks about various probability distributions when determining methods for initializing weights. If you don't care about the why's, it can be used as a reference for coming up with a scheme for weight initialization, but I find it handy to know why my code is doing something so I better know how to tweak it.

-Brian
4 people found this helpful
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Top reviews from other countries

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Guglielmo
5.0 out of 5 stars Ottimo Libro
Reviewed in Italy on January 19, 2014
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Ottimo libro. Illustra tutta la teoria delle reti neurali multistrato e tutti gli algoritmi di aggiornamento dei pesi. Ovviamente consiglio il libro a chi possiede già conoscenze sull'argomento.
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