Reviews (19)

Neural Networks Foundations
This is the first book for everyone interested in the subject. A well-written and well-illutrated encyclopedia of Neural projects, including all the fundamental questions at the forefront of research in Neural Networks. I believe this is the reason for it beeing widely referenced by almost all the authors in the subject.
Review
I bought this book for some research I'm currently conducting, which deals with a wide gamut of topics, ranging from adaptive noise cancellation to fuzzy clustering to image recognition.
Very Mathematical
I used this as a textbook for a Neural Networks course I did in the second year of my undergraduate program in Mathematics and Computing.
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Subjects:  1. Computer Books: General    2. Computers    3. Computers - Communications / Networking    4. Engineering - Electrical & Electronic    5. Neural Computing    6. Neural Networks    7. Neural networks (Computer scie    8. Neural networks (Computer science)    9. Telecommunications    10. Technology / Engineering / Electrical   

Subjects:  1. Computer Logic    2. Digital Electronics    3. Electronics - Circuits - Logic    4. Electronics - Digital    5. Engineering - Electrical & Electronic    6. Fuzzy logic    7. Logic Design    8. Neural networks (Computer scie    9. Neural networks (Computer science)    10. Reference    11. Technology    12. Technology & Industrial Arts    13. Circuits & components    14. Fuzzy set theory    15. Technology / Electronics / General   

Editorial Review

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Reviews (66)

Excellent Introduction and an excellent educational perspective
This book is excellent for a novice in AI. Chapter by chapter - though chapters vary in thoroughness and detail - the book illustrates AI techniques by family by accessibly presenting the problems that motivate the techniques, and the logic applied to implement them (pseudo-code).
wide range of techniques, including neural networks
The authors furnish a voluminous and up to date survey of AI. Covering the gamut of ideas and computational techniques common to the field. Unsurprisingly, there is plenty of discussion about formal logic methods and how to develop inferences in a manner doable by a computer. This is expanded into describing ontologies and how to find knowledge in a learning process. All of these are hard topics that try to attack a fundamentally difficult problem.
Wonderful for its overviews and historical notes
This book was used in both my undergraduate and graduate courses in artificial intelligence.It is a very readable book, and an enormously useful reference.I believe they at least do a cursory overview of the most critical concepts in AI, and if they don't, there are usually great references at the end of the chapter.
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Subjects:  1. Artificial Intelligence    2. Artificial Intelligence - General    3. Computer Books: General    4. Computers - General Information    5. Neural Networks   

Reviews (5)

GreatBook As Far As It Goes
I have used this to get a good background in the topics covered, especially inference theory, and in general I found it to be great book which fills a market gap.The only sins I see are sins of omission.I personally would have enjoyed seeing a more task driven organization.I seem to need these methods periodically but I never seem to need the same method twice.Also, many of the techniques are heavily iterative, i.e., monte carlo, neural networks, etc.This is fine but much of what I do is in the context of simulations where 100,000 step iterative methods don't work so well because of resource constraints.Historically, that has been the problem with many of these methods.They are useful for relatively small domains but don't necessarily work that well for "real" problems. That is probably why more task oriented books are not available.Of course the author is following the outline of the current research into the subject manner which in turn is largely determined by "interesting" and "doable" problems.The real progess in this field will come when the problems are formulated more by what is needed in the nontraditional domains of application. A good example of a useful compression (and identification in some cases) technique that is not covered is Principal Component Analysis.Technically, it is in none of the technique domains covered in this book, but it would have been nice to see some of the methods in the book compared with PCA.The author does make the statement at one point that image recognition is an interesting problem for which the method being discussed at the time is used.Nevertheless, this is a great overview of the subject manner and is very entertaining.That in the long run probably explains the problem: it is a textbook.

A must have...
Uniting information theory and inference in an interactive and entertaining way, this book has been a constant source ofinspiration, intuition and insight for me. It is packed full of stuff - its contents appear to grow the more I look - but the layering of the material means the abundance of topics does not confuse.
Good value text on a spread of interesting and useful topics
I am a PhD student in computer science. Over the last year and a half this book has been invaluable (and parts of it a fun diversion).
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Subjects:  1. Algorithms (Computer Programming)    2. Computer Books: General    3. Computers    4. Computers - General Information    5. Data Modeling & Design    6. Information Theory    7. Neural Networks    8. Programming - General    9. Applications of Computing    10. Computer Programming    11. Computers / Application Software / General    12. Electronics & Communications Engineering    13. Theoretical methods   

Reviews (2)

An excellent overview
In just a decade, Bayesian networks have went from being a mere academic curiosity to a highly useful field with myriads of applications. Indeed, the applications of Bayesian networks are wide-ranging and include disparate fields such as network engineering, bioinformatics, medical diagnostics, and intelligent troubleshooting. This book gives a fine overview of the subject, and after reading it one will have an in-depth understanding of both the underlying foundations and the algorithms involved in using Bayesian networks. The reader will have to look elsewhere for applications of Bayesian networks, since they are only discussed briefly in the book. Due to space constraints, only the first four chapters will be reviewed here. Enjoying this book enormously
Rarely do I find myself reading a technical bookRead more

Subjects:  1. Artificial Intelligence - General    2. Computer Books: General    3. Computers    4. Computers - General Information    5. Neural Networks    6. Computer Communications & Networking    7. Computers / Neural Networks   

Reviews (18)

Too wordy
Having to go thru some of the data mining such as CART, I find the book rather wordy.Sometimes, it takes a couple of readings to understand, too many termiologies.I think a lot of stuff might be better illustrated with mathematical formulae rather than words or both.For e.g., I was trying to understand what is surrogate variable, getting a big picture from the English is okay, but to actually compute it, I find information not as clear.This book is written in a way ... seem to be theoretical, and yet not rigorous enough.
Very complete
This book is a very interesting book to learn the main statistical approach of data mining. It's clear and full of examples. If you go a Stanford data mining website you will find all the courses and exercises linked to the book.
El libro de la mineria de datos
Desde el punto de vista tecnico este libro es sumamente completo ya resume muy bien muchos de los metodos estadistico que se aplican en la mineria de datos. Pero a mi parecer este libro es debil en cuanto a la descripcion de ciertos algortimos que describe, y en algunos casos solo se menciona el procedimiento como una formula (la matriz S de los trazadores cubicos) ya que presupone que el lector ya tiene un buen conocimiento sobre trazadores cubicos.
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Subjects:  1. Artificial Intelligence - General    2. Computers - General Information    3. Database Management - Database Mining    4. Mathematics    5. Neural Computing    6. Probability & Statistics - General    7. Regression Analysis    8. Science/Mathematics    9. Supervised learning (Machine l    10. Supervised learning (Machine learning)    11. Data Mining    12. Inference    13. Mathematics / Statistics    14. Neural Nets    15. Prediction    16. Probability & statistics    17. Statistical Learning   

Reviews (16)

The best book to learn Neural Networks
As an undergraduate Math and Computing student, when I took up a high-level course in Neural Networks as an open elective, the only book my instructor recommended was Neural Networks by Simon Haykin. I got that book, and was soon put off by the mathematical rigour it had.
best for beginnner
I found it is best for beginner with best suited examples. It teaches as if teacher is with you and the way examples seleted is the best. After completion of this book you feel as if expert in subjet with true fundamental knowledge in ANN programming. Best book to start with ANN Programming

helpful
I bought this book ages ago without any knowledge of neural nets and used the book and accompanying neutral.exe executable to conduct a research study.I am not a C/C++ programmer so I cannot speak to the clarity or quality of the code but I did carefully read the text of the book and I found it very helpful in understanding neural nets and applying them.(Readers might complement this book with a good, practical social sciences text on applying regression... for example, Masters and the AI community seemed to have a poor grasp of the phenomenon of "shrinkage" in noisy environments.)
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Subjects:  1. Artificial Intelligence - General    2. C (Programming Language)    3. C++ (Computer program language    4. C++ (Computer program language)    5. Computer Books: General    6. Computer Science    7. Computers    8. Computers - Languages / Programming    9. Neural Computing    10. Neural networks (Computer scie    11. Neural networks (Computer science)    12. Programming Languages - C++    13. C & Visual C    14. Computers / Computer Science    15. Neural networks   

Editorial Review

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Reviews (27)

A good primer to computer networks and its design
A comprehensive textbook for undergraduate/graduate students.The book is complete with basic coverage to different types of networks and their working.Treatment to TCP and other Internet protocols well explained with pseudo code for different algorithms.Various extensions to TCP and network security basics well covered.The book does lack some mathematical treatment to the subject which could have improved the content.

Good for Academic Studies.But...
I am studying this as part of my course.I have found this book teaches you from the base about Networking.It's really helpful in understanding the underlying technologies.Larry and Bruce have done a wonderful job in covering almost all of the aspects.But... I found only one thing lacking.That is...graphical explanations. This book is more of text with required graphics but in some places it took time to visualize the concept.I have had Behrouz A. Forozan's (I hope I have spelt the name correctly)"TCP/IP: The Complete Protocol Suite" along with this.Particularly I found some of the parts like ATM, Routing and other chapters' in this book textually gives us a thorough understanding and Forozan's book does depict that visually in a better way.Here is the bottomline, have this book as "The" texbook and have some other books like the as I mentioned before.

A good general introduction, not an engineering textbook.
Although this book clarifies the concepts of networking quite good, it lacks in-depth mathematical & statistical theory.
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Subjects:  1. Communication Networks    2. Computer Books: General    3. Computer Systems    4. Computer networks    5. Computers    6. Computers - Communications / Networking    7. Internet - General    8. Management Information Systems    9. Networking - General    10. Neural Networks    11. Communications engineering / telecommunications    12. Systems management   

Subjects:  1. Anthropology - Physical    2. Artificial Intelligence - General    3. Computational Biology    4. Computational neuroscience    5. Computers    6. Computers - General Information    7. Engineering - General    8. Engineering Models    9. Medical    10. Models, Neurological    11. Neural Computing    12. Neural Networks    13. Computer modelling & simulation    14. Medical / Neuroscience    15. Neurosciences   

Reviews (2)

I love this book.
This is a wonderfully written, and excellent book. It assumes only minimal backgroundknowledge but imparts a great deal of insight. I love the way that the author describes this area and the connections with deep and beautifulmathematics.

A very nice 'handbook' of sorts for users of SOMs.
The material is presented clearly and comprehensively from the unique perspective of the SOM originator himself. The inclusion ofexhaustive references is particularly useful for the prospective researcher, but, atthe risk of sounding ungrateful, I'm curious as to why paper titles werenot included in the citations? Overall though, a very good reference. ... Read more

Subjects:  1. Artificial Intelligence - General    2. Computers    3. Computers - General Information    4. Engineering - Electrical & Electronic    5. Life Sciences - Biophysics    6. Neural Computing    7. Neural Networks    8. Neural networks (Computer scie    9. Neural networks (Computer science)    10. Reference    11. Self-organizing maps    12. Adaptive and Learning Networks    13. Adaptive und Lernende Netze    14. Biophysics    15. Cluster Analysis    16. Communications engineering / telecommunications    17. Computers / Neural Networks    18. Computers-Artificial Intelligence - General    19. Klassifikator    20. Klusteranalyse    21. Lernen ohne Lehrer    22. Mustererkennung    23. Neuronale Netze    24. Pattern Recognition    25. Science / Physics    26. Selbstlernen    27. Selbstorganisierende Karten   

Editorial Review

Jeff Hawkins, the high-tech success story behind PalmPilots and theRedwood Neuroscience Institute, does a lot of thinking about thinking.In Read more

Reviews (66)

A Great Read on a Very Fascinating Topic
An easy read, yet truly insightful and entertaining. I do believe that most Humans have thought (and do think) about the subject... what makes one person more intelligent than others... why are humans more intelligent than other animals... how come animals don't seem to inherit their parents learnings... one could argue that despite our superior intelligence we seem to be making the same historical mistakes... would be fascinating to explore that topic... do humans lack intelligence when it comes to repeating historical mistakes... or is that due to some other capabilities/attributes taking over?

Open your mind
A compelling, fascinating, and elegant explanation of how the brain may work, as detailed by Treo/Palm Pilot creator Hawkins. Explains the brain and neural networking as one big pattern-recognition algorithm. You'll find yourself analyzing and recognizing your own thought processes and behaviors in a very different way after you finish this. Concluding passages detailing future development of true AI and intelligent machines feature some really exciting stuff.

Exciting and enjoyable
This book is excellent! And intriguing.
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Subjects:  1. Artificial Intelligence    2. Artificial Intelligence - General    3. Brain    4. Cognitive Psychology    5. Computers - General Information    6. Intellect    7. Intelligence    8. Medical    9. Neural networks (Computer scie    10. Neurology - General   

Reviews (1)

Better than I expected
A good introduction to neural nets and their applicability tothe futures markets.Provides mathematical/theoretical basis fortechniques involved in neural net training, testing, chaos and touches on nonlinear systems in general.Also provides interesting benchmarks of several nets against several time series.Great bibliography. Includes Fortran source code for running trained nets but not for training a net. ... Read more

Subjects:  1. Business / Economics / Finance    2. Business/Economics    3. Forecasting    4. Futures And Options Trading    5. Futures market    6. Investments & Securities - Futures    7. Mathematical Models In Economics    8. Mathematical models    9. Neural networks (Computer science)    10. Time-series analysis    11. Economic forecasting    12. Investment & securities    13. Neural networks   

Reviews (1)

More Mathematical than Technical
Defiantly more of a math book than a programming guide, but that was what I was expecting.This book explains how to use neural networks in the field of finance. It does so very logically and mathematically.You are shown how to apply neural networks to many different financial problems.But you are mostly left to yourself to actually implement the neural networks on a computer system.Some example source code is provided for MathCad, which is an expensive software package you can buy separately.
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Subjects:  1. Business & Economics    2. Business / Economics / Finance    3. Business Mathematics    4. Business/Economics    5. Data processing    6. Decision making    7. Finance    8. Investments & Securities - General    9. Neural networks (Computer scie    10. Neural networks (Computer science)    11. Budgeting & financial management    12. Computers / Neural Networks   

Reviews (2)

Comprehensive Book
Independent Component Analysis is a young and interesting topic that gained attention and still receiving more of it. Nice and detailed description of ICA
This is a nice and self-contained book on the subject of independent component analysis (ICA). The authors start with relevant mathematical and statistical background (in Part I) to prepare readers for the derivations of ICA (though seasoned researchers may want to skip the first part of this book). The authors discuss the motivation behind ICA and present several ways to derive ICA (since this subject has been approached by several communities). The authors also compare and discuss the pros and cons of these approaches. The authors discuss several applications using ICA in Part III.Read more

Subjects:  1. Adaptive Control    2. Advanced    3. Artificial Intelligence - General    4. Computers - General Information    5. Engineering - Mechanical    6. Multivariate analysis    7. Neural Networks    8. Principal components analysis    9. Probability & Statistics - Multivariate Analysis    10. Science/Mathematics    11. Systems Analysis    12. Technology    13. Communications engineering / telecommunications    14. Computer Hardware & Operating Systems    15. Electronics engineering    16. Medical equipment & techniques    17. PHYSICS    18. Science / Waves & Wave Mechanics   

Subjects:  1. Computers - Communications / Networking    2. Engineering - Electrical & Electronic    3. Handbooks, manuals, etc    4. Networking - General    5. Neural Computing    6. Neural Networks    7. Neural networks (Computer scie    8. Neural networks (Computer science)    9. Science/Mathematics    10. Signal Processing (Communication Engineering)    11. Signal processing    12. Technology    13. Communications engineering / telecommunications    14. Electrical engineering    15. Electronics engineering    16. Technology / Engineering / Electrical   

Editorial Review

A practical book, Read more

Reviews (6)

A real gem of a book
Some books just have the right feel about them and this is one of them.The author clearly knows neural networks and also knows how to communicate to others in a no-nonsense fashion.With so many books being published, you rarely find a technical book that is as good as this.The emphasis here is on conveying the insights that the experts in this area would know.Importantly, concepts are explained equally in words, graphics and mathematics, maximising the uptake of knowledge from the book. Tufte would be impressed by the quality of the line graphics in this book, and the information that they convey, not to mention the overall presentation.I suspect that this book would make an ideal textbook for a course in neural networks. Overall, I've enjoyed reading it very much.

Neural Smithing
Book is excellent.Covers the theory very well, such that you can make the computer code yourself.They also provide puedocode.You will be able to learn it better than other books that just give you the code. I find that once you understand the theory, writing the code is easy.

Saves you months of information gathering
Everybody who tries to use NNets for real goes through these steps.Read more

Subjects:  1. Artificial Intelligence - General    2. Computer Books: General    3. Computer Science    4. Computers    5. Computers - Communications / Networking    6. Neural Computing    7. Neural Networks    8. Neural networks (Computer scie    9. Neural networks (Computer science)    10. Computers / Computer Science   

Reviews (3)

A bit disappointed because I expected more from this book.
I have been reading through the book. Actually it provides very clear explanations, but I had the impression the author talks too much and keep saying the same things over and over again. The book could be half its volume with the same content of knowledge. Besides the provided examples are a bit too simple and obvious.
Excellent practical book on neural networks using Java
Programming Neural Networks in Java will show the intermediate to advanced Java programmer how to create neural networks. This book attempts to teach neural network programming through two mechanisms. First the reader is shown how to create a reusable neural network package that could be used in any Java program. Second, this reusable neural network package is applied to several real world problems that are commonly faced by programmers. This book covers such topics as Kohonen neural networks, multi layer neural networks, training, back propagation, and many other topics. The content of the book is as follows: Unique book
I have received my copy of the book and I can't put it down. It has been great help with my AI research at the University. I have the other book from the same author "Programming Spiders, Bots and Aggregators in Java" and I have the same comments for both. Both are easy to read, have precise information and great code. Chapter 7 of this book "OCR with Kohonen Neural Network" makes the book more than worth it. Great stuff. I hope the author does not stop and keep writting books like these. I recommend this book for anyone interested in learning AI and also experienced programmers alike. The author makes though topics seem easy. Highly recommended. ... Read more

Subjects:  1. Artificial Intelligence - General    2. Computers - Languages / Programming    3. Neural Networks    4. Programming Languages - Java    5. Computers / Programming Languages / Java    6. Computers-Artificial Intelligence - General    7. Computers-Neural Networks   

Editorial Review

Traditional neural network computing uses continuous propagation of its signals, whereas biological networks use signal timing and frequency in their transmission and computation. Read more

Reviews (2)

relatively current
Pulsed Neural Networks (90's), Artificial Intelligence (80's), Cybernetics (60's and 70's) Telephone Switch Board (10's and 20's) Hydrodynamics (1700 and 1800) it is amazing the names put on cognitive science through the years.This book is a symposium (13 small books) on developing hardware devices capable of replacing or enhancing neurological functions. Using modeling techniques to duplicate biophysical neural pathways can take two forms. The first are math models, which obviously show the relationship between the neurons (virtual reality). The second type is models that build-spiking neurons in real time to which this book is directed. In the first part of the book, a summary of current thought, written by the main compilers of the book (Maas and Bishop) is worth the price alone. The book addresses the question of biological electrical (vs. chemical or genetic) coding, in which the method of information is actually transmitted and received. The compilers have emphasized the chronological event of development with the articles so that the reader does not become lost in which came first.Gravy is given the reader in the form of articles written by researchers in other fields (VLSI) to the point that the reader wonders if one is still reading a book on biophysics. The hard-wired neural net components are then compared to their biological predecessors for the purpose of obtaining usable "dry lab" tools for experiments. ("Dry-wet-electrical lab", "electrical-dry-lab-wet-computer-lab"?). Even though the material contains electrical engineering stuff it is still very readable to biological types and if interested, can muscle through this stuff. The math model development in Matlab is mentioned, but the reference to Matlab's current capabilities in this area is dated (95). Most of symmetries run in the book are older 200 Pentium type machines, and with a faster (650 up) and better busing Matlab's new neural net toolbox can build some interesting stuff (remember however it is still virtual). The "home modeler" can use chap. 7 and 12 as a theoretical basis for stochastic resonance models which the writers, while dealing with stochastic bit-stream overlooked this aspect. However, H.Wilson's Spikes, Decision and Actions is much better. (Matlab interactive).This is a really good book for modelers (reason for the review as opposed to `me to' reviews). Most of the neural nets and circuits designs are easily modeled in Matlab's Simulink to give real time results similar to those reported. (Whether the results duplicate reality is always a question with these types of models). Flights of fancy (the reason for modeling in the first place, at least the addictive part) can then be implemented according to the capabilities of the reader. The book also discusses "hard wired" CMOS chips available replicating biological systems with plug in units to standard computer I/O units (Motorola, National, and Fuzzytech).However a larger question comes from this book. How can the output of a non-deterministic system be modeled by deterministic model (hardware or otherwise) inputs (H.Wilson)?Without a specific knowledge of the role that neural architecture plays in the phase modulations and oscillatory behavior, how can information be transferred by digital or analog devices duplication neural transmission.As the author puts it in Chap. 12, "Furthermore it is not even clear what the goal of a learning algorithm for pulsed neural nets should be; the goal to learn a function or a function (operator). This book is not a failing because it cannot answer this question. Indeed, the avenues it reviews and discusses opens up many more fields and sparks new uses for the fields it introduces.

good introduction
This book presents a general overview to the growing field of spiking neural networks and their VLSI implementation written by many of the major figures in this field. It begins with several clear explications of the spiking response model which has been recently popularized by Gerstner (who has several good entries in this volume) and why looking at such a model might be a good idea (it really is completely fascinating). As with all neural network theory, understanding the model will require a fairly solid mathematical background. The middle portion of the book is dedicated to VLSI implementation (which I am not involved with so won't comment) and the last chapters present a wide variety of articles from the highly mathematical to advice for digital simulation of such networks. Chapter 10 is by far the most mathematical chapter and presents the analytic results that have been derived for a homogeneous fully connected network. Although this is far from a complete reference it provides a clear explanation of the reasons for this direction and enough good references at the end of each section to get you started. I have continually turned to this book while getting started in my research in this area. ... Read more

Subjects:  1. Computer Books: General    2. Computer Science    3. Computers    4. Computers - Communications / Networking    5. Networking - General    6. Neural Computing    7. Neural Networks    8. Neural networks (Computer scie    9. Neural networks (Computer science)    10. Computers / Neural Networks   

Reviews (2)

An excellent book too
This is actually a must-have book for those who want to study pattern recognition.

excellent tools for implementation of P.R. techniques
i first bought the book by Bishop (Neural Network for Pattern Recognition) and anyone who have read it can tell u how excellent that book is. This book has a little bit less theory and more on implementation which is perfect for me. This book include all the topics covered in Bishop and then some. How the book is organized, and how concise, easy to understand the material is at the same amazing level as Bishop's. I believe implementing and practicing things u learn is key to understanding them.. if you just look at how things are implemente