Neural Networks for Pattern Recognition Review

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Neural Networks for Pattern Recognition ReviewThis book came out at about the same time as Ripley's, which has almost the same title, but in reverse. At the time, I liked Ripley's better, because it covered more things that were totally new to me. Then a friend said he had chosen Bishop for a course he was teaching, and I went back and reconsidered the two books. I soon found that my friend was right: Bishop's book is better laid out for a course in that it starts at the beginning (well, not quite the beginning--you do need to be fairly sophisticated mathematically) and works up, while Ripley's is more a collection of insights all at the same level; confusing to learn from. Bishop is able to cover both theoretical and practical aspects well. There certainly are topics that aren't covered, but the ones that are there fit together nicely, are accurate and up to date, and are easy to understand. It has migrated from my bookcase to my desk, where it now stays, and I reach for it often.
To the reviewer who said "I was looking forward to a detailed insight into neural networks in this book. Instead, almost every page is plastered up with sigma notation", that's like saying about a book on music theory "Instead, almost every page is plastered with black-and-white ovals (some with sticks on the edge)." Or to the reviewer who complains this book is limited to the mathematical side of neural nets, that's like complaining about a cookbook on beef being limited to the carnivore side. If you want a non-technical overview, you can get that elsewhere (e.g. Michael Arbib's Handbook of Brain Theory and Neural Networks or Andy Clark's Connectionism in Context or Fausett's Fundamentals of Neural Networks), but if you want understanding of the techniques, you have to understand the math. Otherwise, there's no beef.Neural Networks for Pattern Recognition Overview

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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems) Review

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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems) ReviewWitten and Frank have generated a book that is readable without eliminating all technical (yes, even mathematical!) descriptions of the key data mining algorithms. And they are up-to-date, including support vector machines and boosting. There are sufficient examples of the techniques to provide readers with a good feel for what each technique can accomplish. For example, how many books can provide a readable explanation of support vector machines?
There are some quibbles, such as not including any discussion of neural networks (noted in Ch. 1 with another reference)--I believe it deserves some attention because of its widespread use. Additionally, future editions should include a least a brief summary of data preprocessing, input selection, feature creation, etc. But these are quibbles.
The Java portion of the book is not of as much interest to me, but for those wishing to implement the algorithms, it provides a nice blueprint (from the code I looked at).
For what they have undertaken, they have performed admirably, and I would highly recommend this book.Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems) Overview

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Purely Functional Data Structures Review

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Purely Functional Data Structures ReviewOkasaki's slim volume is one of the best expositions on implementing data structures & algorithms in a functional language. After taking an introductory course on functional programming, this would be the book which tells you where to go next.
This book doesn't just present a rehash/rewrite of imperative data structures, only written in a functional language. Instead, Okasaki makes sure to emphasize benefits which only functional programming can bring to the table. For example, many functional data structures can compactly represent not just their current state, but all of their past states as well--a feature called "Persistence". Also, functional newbie programmers might be wondering why lazy vs. strict programming is a big deal, and Okasaki shows clearly where data structures can benefit from either being lazy or being strict.
For the advanced reader, Okasaki also presents several powerful techniques for analyzing the runtime of algorithms, including the so-called "Banker's Method" and the "Physicist's Method" for analyzing amortized algorithms.
I hope that Okasaki comes out with a 2nd edition of this book; there is one missing piece in particular which I really wish he would have included: Although he presents an EXTREMELY lucid description of how to implement Red-Black trees in a functional language, he only presented algorithms for insertion and querying. Of course, deletion from a red-black tree is the hardest part, left here, I suppose, as an exercise to the student. If you want to supply this missing piece yourself, check out a paper by Stefan Kars, "Red-black trees with types", J. Functional Programming 11(4):425-432, July, 2001. It presents deletion routines, but you'll still want to read Okasaki's book first, for unless you're very much smarter than me you won't be able to understand Kars' paper until you read Okasaki's exposition of red black trees.
Finally, this book is not just useful for programmers in functional languages; logic programmers, using prolog or a varient, will also find this book very helpful, because most of the techniques (all of the techniques, really, with the exception perhaps of the lazy programming stuff) can be directly applied in a prolog programming setting as well.
After reading this book and implementing some of the data structures for yourself, you'll be amazed at how fast algorithms can run, even when written in a functional language!Purely Functional Data Structures OverviewMost books on data structures assume an imperative language such as C or C++.However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme.This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures.The author includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages.All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.

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