Rating:  - the book is really perfect... but the shipping was bad!!!
The book is really perfect and is exactly I hoped.
However, the main and only bad critic I want to highlight is the shipping.
The package was very light and the book cover arrived damaged: the corner were maltreated.
No really, only a box including 2 plastic bubbles is not serious and totally insufficient to correctly protect such kind of nice books.
Rating: - Excellent
An excellent book. The only comment that I have that has not been covered is that you should know c++ templates to use this book... theres a problem with that, most if not all template books require you to know STL. You will have to read both in conjunction if you do not all ready know templates.
Once you get past that little pit fall though, this book is invaluable to any c++ programmer.
Rating: - C++ Programmers: Don't try to live without this book
This is far and away one of the best written programming and reference books I have purchased in 20+ yrs of programming.
It gives a perspective on state-of-the-art C++ style, language constructs, and technique that only a choice few people in the industry can offer. The book has all of the meticulous attention to detail and design formalities that one could expect of the finest academic.
Bravo, Mr. Josuttis, and THANK YOU!
Rating: - Its title says it all
It is a tutorial and a complete reference at the same time. I already knew very well STL when I have read this book but reading this book has been very enjoyable because I really appreciated its format. The tutorial and reference part are not clearly separated in 2. I hate books where you never read the reference part because it is as fun to read than reading a phone book.
Of course, the first part is strictly tutorial where it introduce STL, its basic principles and a quick overview of all the services provided by the library to the reader. Then lengthy chapters follow to cover containers and algorithms. This is where the book shines. It covers one by one each container and each algorithm and to support their description, a small sample program follows.
Before this book, there were some algorithms that I could not figure out exactly what was their purpose or how to use them correctly just from their description in the STL man pages. By reading the samples source code of this book, I had many 'AhAh' moments where finally I could understand some algorithms less frequently used. For all there reasons, I am very happy with my decision to get hold of this book.
Rating: - Stop searching, this is the one you want
Yes it's long, but I have found this book faster to make use of than shorter books on the topic of the Standard Template library (STL). This is the book you want.
Read Ch 2, scan Ch 3. Ch 4 can wait, page through, don't read Ch 5. Read Ch 6 and just note how iterators can be used in Ch 7. And you are done. This can be done in under 3 hours. Then use the rest of the book just as a reference as needed and you will be an STL user eventual expert.
After using STL, you will never go back -- it transforms C++ into a useful language rather than a memory management sink hole. Oh, then you will want to go on to the new proposed, but not yet accepted (as of March 2007) standard library extensions. For that, see "The C++ Standard Library Extensions" by Pete Becker The C++ Standard Library Extensions: A Tutorial and Reference
What do the extensions (sometimes known as "Boost" library) add that's missing in STL? Well, Hash functions (how could these have been left out?), tuples rather than just pairs. Pairs in STL allow you to treat items as a unit -- very useful for database and pattern recognition/association for example. Tuples extend this to lists of items. Pointers with reference counting -- Speed up your code by easily avoiding needless copying and have the memory auto delete when all references to it go away. Doesn't solve the problem of "fatal embraces" where references point to each other, but it helps a lot.
What's still missing? By now, decision trees are just so mature and useful that they ought to be built in along with statistical boosting, k-means and agglomerative clustering, K-D trees for nearest neighbor association. That is IMHO, data ought not only allow methods to be attached, but clustering and basic machine learning/prediction should just be built in and standard by now. The above routines are mature and a basis of much more advanced routines.
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