Algorithms Books

The author has great suggestions for teaching students from different countries, and "hands-on" approaches that these students will understand. The algorithms that are shown from different parts of the world are truly wonderful. They not only show how other countries perform operations and help others understand the procedures, but are great as alternative methods for struggling students. Some of my students understand these methods better than the traditional (especially long division!). Kudos to the author.

Good book lot of interesting details. Helpful for understanding parallel systems and algebra algorithms.

This is an easy and mostly entertaining read. The author uses many anecdotes to
persuade us that statistics can be a useful tool for decision making. Some of
the described applications use lots of data and multiple regression. Those are
easier to do now than they used to be, because more data is collected and kept.
Some are trivial. If your company hurts a customer, apologize. You might get
some ideas of thing to do that might help your organization. You will not get
any detailed help about how to implement the improvement, but there is a good
chance there is enough information that some systems person can figure out what
other skills are needed to make the idea work.

There is some discussion of limitations on the methods, and some warnings about
potential abuse, but not enough. Ayres seems to confuse correlation with causation.
He also frequently assumes the sample is representative of the population.
Even when trying to make the sample representative, it often is not. He also
assumes the answer is in the data. Sometimes it is not. Ayres reports a study
concluding widespread point shaving in college basketball because a distribution
at game end did not match the distribution five minutes earlier when a highly
favored team was ahead by about the spread. I have no opinion about the conclusion,
but the simpler explanation of the coach thinking it was late enough to safely
let the weaker players participate more was not considered.

Regression is a powerful tool, but it is easy to misuse. For an ongoing
survey of misuses, see junkfoodscience dot com, a blog. Many of the entries show
the flaws in statistical claims of medical trials. Also try stats dot org.

The answer is of course: a lot.
And Ian Ayres' book will tell you a little about it.

Supercrunchers are those who use lage datasets
to find patterns in human behaviour, and
predict the future based on these large datasets.

The book informs us that super crunching is on the verge of being
used all over. E.g.
Chess grandmaster Kasparov was no match
for IBMs Deep Blue chess computer,
that stored some 700.000 grandmaster chess games to help find the
winning move.
The IRS could use its data to tell a small business,
if it is spending too much or too little on advertising.
Indeed, the IRS probably has enough data to
make good estimates on whether business, marriages, etc. etc.
will fail - based only on comparison with its existing dataset.

For the paranoid, it is a horror that supermarkets could map your life cycle and predict your next purchases pretty accurately (based on
what other similar customers did).
For the optimist data mining is a good thing and we'll all lead better lives because of it.

Want to write a bestseller about it? Compare your title and some key words with data from a database of books, titlescore.com, containing millions of bestsellers and flops, and you will get your answer.

It all seems pretty straight forward, and the book has some nice examples of what we can expect in the coming years.

-Simon

Ayres demonstrates how statistical analysis of large datasets is affecting the way the world works in a broad range of applications: credit card companies, sports teams, wine critics, development economists, medical practitioners,* law enforcement agencies, schools, etc. "Freakonomics didn't talk much about the extent to which quantitative analysis is impacting real-world decisions. In contrast, this book is about just that - the impact of number crunching" (p13).

As an economist, some of the work is familiar (for example, the research Ayres and Steve Levitt did on the value of the vehicle-recovery device LoJack or the Poverty Action Lab), but Ayres gives a good introduction for the uninitiated. And he covers such a broad range of applications that I learned a great deal.

Like other research surveys (Freakonomics, The Tipping Point, Blink, Stumbling on Happiness), I view these books mostly as surveys of interesting research. Each has a central thesis (Ayres' is that traditional intuition and expertise will be - or already has been - replaced by computing power and will have to learn to complement that power rather than compete with it) which may or may not be convincing, but the books tend to be good rides because so much of the surveyed research is interesting. (For example, I'll be studying more about Direct Instruction - a scripted way of teaching reading that may be useful in my own work - based on this book; and the model Ayres expounds of how private firms learn from iterative experimental trials may apply well to some of the agencies I engage.)

As far as Ayres' thesis goes, I find him relatively convincing (computers with lots of data do predict many things better than people**) but despite his many caveats, the tone should probably have been more humble. He doesn't - for example - explore the issues brought by Taleb in The Black Swan: The Impact of the Highly Improbable, how traditional statistics may be worse than useless in financial markets where a single, completely unpredictable bad shock can wipe out years of carefully predicted investments.

This book was lots of fun to listen to, not least (unintentionally) because Ayres loves giving irrelevant but amusing descriptions of his researchers. The examples below are all economists:

"Ashenfelter is a tall man with a bushy mane of white hair and a booming, friendly voice... No milquetoast he" (p2).

"Even now, in his forties, Larry [Katz] still looks more like a wiry teenage than a chaired Harvard professor (which he actually is)" (p65).

"Esther [Duflo] has endless energy. A wiry mountain climber..." (p73).

And of course you know this is the Freakonomics family because of the Levitt-love scattered here and there: "There is a new breed of innovative Super Crunchers - people like Steve Levitt - who toggle between their intuitions and number crunching to see farther than either intuitivists or gearheads ever could before" (p17).

I listened the unabridged audiobook narrated by Michael Kramer (not Michael Kremer - quoted in this book on p74), published by Books on Tape (6 CDs). Kramer does a good job except when he tries an Australian or British accent.

* For an excellently written description of evidence-based medicine and more, read Atul Gawande's Better: A Surgeon's Notes on Performance.

** One of the most striking findings comes from the meta-analysis (1996) of two psychologists, Meehl & Grove, who look at 136 studies comparing human judgment to equation-based judgment. In only 8 of the 136 studies was expert prediction found to be appreciably more accurate than statistical prediction." Overall, experts got the predictions right 66% of the time whereas Super Crunchers got them right 73% of the time. And the 8 in which experts did better weren't concentrated in any particular field. From looking at the paper myself, I found that 64 of the studies favored the Super Crunchers whereas 64 found the two methods roughly equal. Noteworthy. [In the book, p111 and p232.]

This is new? The notion that empirical research is useful has been dealt with in book after book. The book not only recycles stories word for word without quote marks from the New York Times and other publications. There are hundreds of books that show that empirical work can help understand the world. What is new? What is interesting that is new here?

It comes on the heals of some really great non-fiction analytical books. Unfortunately, this book is all anecdotal and lacks real substance. It is good for non-mathematical, non-analytical people, but not good for people with solid educations in math, statistics, and data analysis.

This is an okay book. The algorithms are described in great detail (actually sometimes too much detail), moreover it is not very mathematical. For this reason it could be the choice for the readers fed up of the math in other algorithms books e.g., Cormen et al's.

However the biggest complain I have with this book is that it is overloaded with too much contents. When things could have been described in a few lines, dozens of paragraphes have been used for it. The reader just losts in the text. And interestingly the job is still not done, i.e., the reader is still not able to follow the algorithms easily.

Also for this reason it is not a good choice for someone wants to get a quick overview or revision.

I don't intend to read this book from front to back (well maybe one day if I get really bored) but I think this book does a very good job explaining the algorithm without getting obsessed with overly formal mathematical games. In my opinion the graphs are the most important part of the series, since these are the algorithms and data structures that usually AREN'T included in a programming language's libraries; STL for example. You will find many of these topics in a mathematics and statistics program (how I first encountered them) so the book does get mathematical, but out of necessity.

4 out of 5 stars for sometimes being unclear.

I had to teach this course and must see it is the pick of the books I have seen on this subject though it is far from ideal. Why can't these people use meaningful variable names and comment the code. Is it the authors intent to teach bad programming practice.

i feel writer is confused and writes very jumbled together spaghetti code.
to tough to uderstand
horrible reference book

I'm a learned industrial engineer in electronics but got into informatics right after my degree. So I never had a course on algorithmic and had to learn nearly everything by myself.

After some years of working in the field, I tought it was maybe time to get some background on the subject so I got this (now an outdated edition) of the book.

Well, it was the book it took the longest to me to finish in the informatics field.

The book explains a whole bunch of basic and more advanced general-purpose algorithms, and so has a good coverrage of the subject.

However, there are two problems with the book:

1) The coding style is very bad: the author likes to use global variables, and variable names are often very cryptic. Example:
* p = parent
* g = grandparent
* gg = greatgrandparent
* c = child
* x = current node
* y = temporary node
...

2) You cannot read this book's chapters in a random way: you have to follow the chapter ordering, because often knowledge of later chapters is based of knowledge of earlier chapters, and, because of the bad coding style you have to often remember the meaning of the cryptic variable names several chapters later when they are reused. If you're like me, you've forgotten the meaning, which means reread that damn chapter, which in turn can again be based on an earlier chapter. You get the picture why it took me so long?

Like other later short works, this is a little gem. Bellows account of an old and futile love is full of the kinds of regrets and second guessing that goes with old age. Well worth a read and fully satisfying.

This novella, to me, was very hilly in that every 7 pages or so, Bellow would write anything between a line and a page that were perfectly brilliant, but the pages in between seemed to just go on and on about the lives of socialites and rich Chicago Jewish families.
This wasn't particularly enthralling, and I suppose that I had higher expectations for the actual prose used in the novella, but overall I felt that the story, if somewhat delayed, was satisfying and altogether not a bad read.

The Actual is a short, supple book by Bellow, in the late period of his writing life. Though barely over 100 pages, the writing roils and heaves over a vast terrain of the inner consciousness. Harry Trellman is classic Bellow - mind constantly active, noticing the details of human character, individual lives all the while. The Bellow contempt for little lives comes out - a marvellous passage in which Harry admits that he finds some neighbours of his 'commonplace' people, products of mass democracy, fornicating and earning money. But Adeltsky, the multi millionaire, for whom Harry has a curious job as a 'brains trust' - an sort of intellectual confidant, a fellow man of brainpower magnitude, makes up for this with his large scale American raw capitalist mind.

The other main character, Amy, whom Harry has a long history with is presented well in the Bellovian female mould - wily, elusive, pungently attractive. The ending is a little pat and upbeat, but the novel satisfies as a smaller dose of how Bellow can move a reader and stimulate the synapses.

Saul Bellow is no longer with us. And as he is a favorite writer of mine I always wish there were more of his work for me to read. I thought I had read all the novels, and so was pleasantly surprised when I came across 'The Actual'. I read it in a single sitting and it brought great pleasure.
There are many of the usual Bellow virtues displayed in this book. His descriptive genius, his feeling for the 'things' of this world, his capacity to create interesting characters, his philosophical analysis of human relations, his sense of the complexities and contradictions of the human soul, his bright and colorful always zesty language, his sense through the telling of the story of individuals of connecting us with larger worlds meanings problems, his mixture of intellectual and business types, his vast worldliness and worldly knowledge, his great feeling for the nuances of American and modern civilization.
This tale is told by another of Bellow's middle- aged Jewish alter egos. And involves another of his love stories, this one having begun in adolescence and kept alive in imagination through many years comes to a surprising somewhat happy ending. The minor characters are also brilliantly drawn.
Perhaps the impression is mistaken but after reading Bellow I somehow feel I know more about life, understand something I did not before.
A great read.

I am a Bellow fan and have read 12 of his 13 novels, and created an amazon guide: "A Guide to Reading Saul Bellow."

In case you are new to Bellow, his novels reflect his life, his writings, and his five marriages during his five active decades of writing. He hit his peak somewhere around the time of "Augie March" in 1953 and continued through to the Pulitzer novel "Humbolt's Gift" in 1973. He wrote from the early 1940s through to 2000. His novels are written in a narrative form, and the main character is a Jewish male, usually a writer but not always, and he is living in either in New York or Chicago. Bellow wrote approximately 13 novels plus other works. Bellow progressed a long way as a writer over the five decades. This story was written near the end of his career in 1997 and is nothing like the early novels "Dangling Man" or "The Victim" written 50 years earlier. Those were heavy slow reads. "Dangling Man" is often boring, and Bellow was in search of his writing style in that period of the 1940s. The present novel is light reading, written in an easy to follow style and is just over 100 pages, barely more than a short story. It has some merit but it is a far cry from the brilliant writing of "Herzog" or the entertaining read "Humbolt's Gift."

What was surprising for myself was the very slow start to the book. The first 20 pages or so seem a bit aimless, and it is not until the central character Harry, a retired businesman re-unites with his teenage flame Amy Wustrin, that the story takes off. They meet by chance and work to help a Chicago millionaire and to look after the burial of Amy's dead husband. As in other Bellow novels, there is a lot of self examination and many recalls by Harry of past memories of the times that Amy and Harry spent together in their early years - decades earlier as teenagers.

The slow pace picks up in the second half and it has a surprise ending. To explain the title would be to explain the plot and surprise ending. It is an interesting read but definitely a notch or two below most of his other works.

This is an interesting Bellow read, but not the first that I would recommend by Bellow. It lacks the charm, the prose, and the complexity of some of his other novels written between 1950 and 1980.

Reading this book was painful for me. This is coming from a reader of the English version. The idea of this story could have been good, a chase to save a forbidden treasure from a greedy man with a lot of wealth, but I did not find it at all interesting.

I can understand why this author is well liked, some parts of the book were well thought out and described. The problem was that the main story was not well planned out. All of the story fit together awkwardly and made it uninteresting to read.

Mystery and fantasy are not a category that I believe should be written by this author. The magic in this story seemed to far-fetched and too all-powerful to make it fun. Although some may have been interested to find out who the villain was, I thought that it was painstakingly obvious from the moment the character appeared. If you never have read a good mystery novel in your life, you may be fooled.

I have read books that were translated into English before and I understand that some of the creativity may have been lost. I think one star is appropriate for the book since I do not understand how the amount of creativity this story lacks could have been lost in translation. If the story is lost in the translation, I feel that is should not have been translated in the first place.

Kingdom of the Golden Dragon by Isabel Allende is very interesting. The plot had many twists and turns that really spice it up. How the plot weaves together and who the bad guy really is was awesome! How the Golden Dragon really works is brilliant! All of the settings, from India, to The Kingdom of the Golden Dragon was interesting. The author portrays the love and compassion of the Nepalese people that they show for each other. She also shows how they can be cruelly exploited. This among the best children's books I have ever read. By JJ

I loved this book on tape and its two companion stories. I've always been a fan of Isabel Allende and listened to Daughter of Fortune and Portrait in Sepia in the car. Then Id purchased Kingdom of the Golden Dragon and the the two other books in this series on tape. I really enjoyed them although they are very different from Allende's other books. I imagine that a 10-13 year old child would really enjoy these books and have set them aside to give as a gift to a special young person in the future. Despite the more simple story line I loved the tapes and couldn't wait to get back in the car to listen. Books-on-tape are, I think, the complete answer to road rage. When I'm listening to something good I don't care how long it takes to get where I'm going.

I enjoyed reading this book because of Isabel Allende's ability to recreate and capture the desolate and harsh environment of the Himalayas. This is present at the beginning when the two Monks are traveling about the mountains and discover the Yetis for the first time. I also liked the idea of having the protagonists evolve in terms of their courage and attitude towards protecting the statue. This is important because you should always fight for what you believe in and sometimes making the decision to go on is difficult, but in the end, it can make a huge difference.One of the few things that I disliked about this book is that sometimes the story moves at a slow pace and the plot becomes predictable to the reader. Overall, I would highly recommend this book.

Only after I finished this book did I realize it was for young adults! (My reaction before I found out was that Allende had gone seriously downhill.) But from the young adult perspective, it's not a bad read. The second of three mystery/travel/adventure books by Allende, written for her grandchildren, this is an entertaining tale set in a thinly disguised Bhutan. An irascible, mid-60's-ish travel writer named Kate Gold invites her grandson and his friend Nadia from South America to join her on a trip to the Himalayan Kingdom of the Golden Dragon. There they meet up with the King and his heir, a seven-foot tall monk trained in the martial arts, the secret Sect of the Scorpion, a plot to steal the Kingdom's most valuable treasure, etc. No sex, not much violence, and lots of traditional values. Probably good for the 12-13 year old set, but, unlike Harry Potter, definitely not for grownups.

Even if it is bad as all the gentlemen said, I think at least it gives me many examples which are not mentioned in other books before.

I am a professional in the field of data mining (over 10 years experience). I am always taking classes and reading on the subject. This book was a required text in a grad class I was taking in the evenings. I was excited because I had heard so many good things about it.

The book is indescribably bad.

It is bad if you want theory.
It is bad if you want practical advice.
It is just plain bad.

BAD! BAD! BAD!

Do yourself a favor and go out and get the books by Gordon Linoff et. al. (Mastering Data Mining and Data Mining Techniques). I believe that Amazon will sell you both for not much more money than if you buy this book. Either one of those books is better than this! (I recommend buying both, you won't be sorry!)

SAVE YOUR MONEY, AVOID THIS BOOK !!!

This book is not an introductory text. Anyone interested in a particular topic should consult the preface of the text to find out what it is about. The negative reviewers were not fair to the authors on that score. Had they read the preface they would have found out (1) how the authors define data mining, (2) that they see it as a subject with an important mix of statistical methodology and computer science and (3) that it is intended as an advanced undergraduate or first year graduate text on the topic.
They also provide a very well organized structure for the text that is well described in the preface. It consists of three parts. Chapter 1 is an essential introduction that is informative to everyone. Chapters 2 through 4 go through basic statistical ideas that statisticians would be very familiar with and others could view as a refresher. The authors have experience teaching this course to engineering and science majors and have found that many of these students unfortunately do not have the prerequisite statistical inference ideas and need this material covered in the course.

Chapters 5 through 8 cover the components of data mining algorithms and the remaining chapters deal with the details of the tasks and algorithms.

The book features a further reading section at the end of each chapter that provides a very nice guide to the useful and most significant relevant literature. The author's have done a very good job at this. One mistake I found was a reference to Miller (1980). I think this was intended to be a reference to the seocnd edition fo Rupert Miller's text "Simultaneous Statistical Inference" which was published in 1981 by Springer-Verlag but the full citation is missing from the list of references in the back of the book.

This book deserves 5 stars because it does what it intends to do. It presents the field of data mining in a clear way covering topics on classfication and kernel methods expertly. David Hand has published a great deal on these techniques including many fine books.

Mannila and Smyth bring to the text the computer science perspective. There is much useful material on optimization methods and computational complexity.

Statistical modeling and issues of the "curse of dimensionality" and the "overfitting problem" are key issues that this text emphasizes and expertly addresses.

The only thing the text misses is details on specific algorithms. But I do not grade them down for that because it was not their intention. They emphasize methodology and issues and that is the most critical thing a practitioner needs to know first before embarking on his own attack at mining data.

The text does provide most of the current important methods. Although Vapnik's work is mentioned and his two books are referenced there is very little discussion of support vector machines and the use of Vapnik-Chervonenkis classes and dimension in data mining. The new book by Hastie, Tibshirani and Friedman goes into much greater detail on specific algorithms include some only briefly discussed in this text (e.g. support vector machines). The support vector approach is also nicely treated in "Learning with Kernels" by Scholkopf and Smola.

I highly recommend this book for anyone interested in data mining. It is a great reference source and an eloquent text to remind you of the pitfalls of thoughtless mining or "data-dredging". It also has many nice practical examples and some interesting success stories on the application of data mining to specific problems.

It's not that this is a bad book, but you have to make sure you are right audience. The book offers very high-level overviews on various techniques of data mining, but it is almost impossible to learn how to really implement them. Since there are no exercises after each chapter you probably already know who the target audience of the book are.

Very good book for a general overview of data mining algorithms. Covers a wide variety of DM approaches.. however lacks concrete examples to clear concepts thoroughly. I especially liked Chapter-5 which gives a general framework to look at any DM algorithm. This clears confusion created by so many diverse algorithms with overlapping concepts and applications.

this isn't worth the price. it's comprehensive, but often belaboredly so. there are concepts that are introduced too early in the book and might not be of immediate use to the beginner. this book could be more concise, physically lighter, and cheaper.

Whenever I go to learn a new language, I always look to see whether there is a book from Thompson's, 'From Problem Analysis to Program Design' series available on that language. This style of textbook always seems to get it right when it comes to academic learning.

After trying a couple of C++ texts, I found that with this particular book, I could understand those niggly bits of the language that can be very hard to explain/learn. This book has brought me much further than I was able to go with other texts. Clear explanations and useful case studies make for the best way to learn a language. Especially the case studies, as they show you how you can use what you have learnt constructively.

My advice: learn C++ with this book, and use C++ Primer Plus (5th Edition) as a reference companion.

I took an introductory c++ class a few years ago and the textbook for that class was much more difficult to follow. Malik does a great job going over things so that difficult concepts are easily understood. If you are a beginner you may have a hard time understanding some of the concepts in this book. However, learning to program takes effort to learn well and the lengthy code examples in this book are an excellent way to see overall programs instead of just snippets. Some reviewers complain about the math in the book. There is actually very little and what there is fairly basic algebra. You can skip through most of the math problems and be fine. A good use of color as well.

I have just completed my order with Amazon.com for a copy of this book, so I don't have the book yet. Why the decision to purchase ? Well, I read the reviews here and decided to download the source codes from the author's website and see what the fuzzes are all about. I opened up my Ultra Studio and start going over the codes. Holly smoke, these stuffs can saved you hours of programming, the author's got items on stacks, queues, linked list, etc. I read some sample writing from the website, and the writings do seem more accadamical than an easy read on a Sunday afternoon. For me, this is fine, as I am used to this type of writings.

Overall, seem like a safe purchase.

This book presents C++ programming in a fairly rigorous fashion. Many of the examples are somewhat long, however, this is what happens when you need to move beyond simple stuff like finding the maximum number in an array, counting a series of numbers with a loop, printing a triangle of asterisks, and so on. Such problems are examined in this book but the author doesn't stop there. This is one of the few books that actually demonstrates the kind of programming skills that the "real world" demands.

Things I didn't know when I ordered this book was how structured the book was. It's easy to comprehend and doesn't contain too much unnecessary information. Furthermore, what I also managed to miss was the disc which was included, and this contains more in-depth code, which allows one to check a whole program instead of only a part.
It is definitely a good decision to start with buying this book if you're studying Algorithms with C, since this will truly help and support you on your way.

The book is probably OK, but there are better, much better ones on the subject. I highly recommend looking at "The Algorithm Design Manual", just search Amazon and you'll find it.

compare to most algorithm/data structure books out there, this book is not as complete as those, but it's much easier to read, and diagrams in this book is well drawn and much eaiser to follow. Why would I only give it 3 stars? One thing really ruined this book - obviously the publisher/editor/author try to increase the total page number by putting ridiculously big comment block in sample code(single line comments takes about 5 lines, all surrounded by '*' and spaces) This made the sample code difficult to read, imagine a 5 line function has to be printed in 2 or 3 pages.

To be fair and honest, I have not read this entire book and that is because I've read far better ones. If you are interested in this topic (and if you're a programmer or computer scientist, you should be), look to Robert Sedgewick's excellent "Algorithms in C" in two volumes, or "Mastering Algoritms" by Cormen, Leiserson, Rivest, and Stein. While the algorithms in Cormen, etc.'s book are not in C, they are in a pseudo language that easy to convert to C. I'm really, really glad I looked this book over before flushing my (limited) money away.

The book contents is good, the algorithms presented are more or less well explained and the implementeations themselves are not bad (but could be better).

Unfortunately this book has 2 mayor problems:

Sometimes you need an implementation of an algorithm for which you already know the inner-workings, just need quick code instead of reinvening the wheel yourself... the book will not allways give you that, it will sometimes build an algorithm based on previous ones! Darn!, I am supposed to go straight to the point I want and get the code without having to read a couple of previous sections.

Second and worst of all is the coding style this guy has. I don't know what the other reviwer that said that the code is great programs in but certainly not in C. The author of the book simply has the worst style ever... look at the comments, a one line comment surrounded by a box!!! give-me-a-break!... where did he learn this? He should read a book about style, perhaps read Code Complete by Steve McConnel or something before attempting to write code. Anyway this is just one of the many style flaws this book has.

If I could I would return it, after all, you can get mostly any implementation from the internet (I had to do that or would have wasted lots of time and... time is money).

If well written, the book would have been 1/2 its size and then it would have been good.

Why 3 stars? Well, in spite of the poor programming style and bad presentation of some algorithms, if you have time and patience, you get someting out of the book. Just don't use the coding style he uses... if you try that at work you would be fired or at least laughed at.

I am using this book for a introductory course in Data Structures. It wastes far too many pages on specifications of ADT's. There are pages upon pages of data structure specifications, and very little on showing how they can be used in a real program. There are much better books out there. I like Data structures & algorithms by Robert Lafore. Lafore gives example programs that use the data structure being taught. To me, the specifications are utterly useless. I had to buy a supplemental book to finish my course, as my professor was bad, too. Look elsewhere, there are better books.

For the student seeking to learn data structures, this book certainly covers the basics (sorts, trees, graphs, recursive elements, stacks, queues) well. While some introductory Java is enforced, it assumes a general background in basic Java programming.

The book is not very useful. The author jumps between Java versions but never covers all of the new features Java 5.0 has to offer.

Despite having a professor who criticized Main's approach to almost every topic (seemingly in a bid to sell more of her own book), I enjoyed this book and got much use out of it. Other reviewers here claim the example code was buggy: I rarely type in and compile sample code - I never did for this book - so I cannot comment on any errors. However, the author's website has code for almost every class in the book. I highly encourage you to check it out.

I enjoyed his discussion of the topics; he clearly explained the fundamental ideas of the topics covered in the book. One does not need to have example code to write a linked list class if one reads his clear descriptions of it. Same goes for most ideas in the book.

The weak point I thought was sorting, and this was more of a weakness of my own than the author's. Two entire chapters are devoted to searching and sorting, but I just wasn't very interested in it. However, it is a useful concept, and you get much analysis of a few common searching & sorting routines.

The best strategy to use this book is simply to read it straight through. Only quickly scan his code, to get an idea of one way to implement an idea. Read his explanations a few times until you understand the ideas and can state them in your own words. You don't need to be able to memorize Java-specific implementations of ideas from this book. You should, instead, be able to clearly explain in English the abstract ideas that are taught in this text. Recommended both for class and for learning on your own.

I read a lot of confusing books by java experts that sometimes forget to go back to the simple basics when teaching simple minded readers. However, this book is for continuing readers of java who had some, but not expert training, experience in writing programs. What I liked most of this book is how he gives pointers on what to look out for while programming, and provides alternate solutions that would be better off in the long run than what general programmers tend to come up with. I also liked how he distinguished between specification and implementation to improve group/team environments (especially for large programs). His specifications before each class gave me a very intuitive idea of what is being asked rather than being confused with all that code. Once I got the general idea, I was able to under the code more. By following the convention that I described about the way he helps the readers, data structures became very clear to understand, especially when it comes to implementing such structures in Java. Its better to read this book slowly one chapter at a time than to jump into the middle, if you want to get a thorough established view of what is going on.