Learning to go the extra step in modeling.
344517
Plenty of learning value and an incentive to try more than you might think you are capable of. Reasonable price.
4969643
This book starts out with a nice basic introduction, perhaps a little too basic for me, but nonetheless it began to provide many valuable examples and tutorial on basic image processing techniques. I had to learn image processing to develop my own algorithm for object tracking and recognition, with the help of this book I was able to take a combination of a half dozen or so separate approaches to develop my own approach for my specific application. It is nice that it is not entirely MATLAB focused, and they do go into some of the mathematical operations behind the filters for those interested (which I am), and then follow it up with direct MATLAB implementations. I wish it would go into some more advanced classification routines for color image processing from video, especially Kmeans classification, but otherwise it is a great book.
3848737
I got the book a few days ago and am very satisfied with the purchase. I started learning Blender a few months ago and have gotten to the point where I know where everything is, but was a little shaky as to what the methodology was in building a complex model. For an intermediate user this book is gold. It shows you enough to get you going but not the really basic stuff which was not necessary. Was was great is it gives you a system
for breaking down complex models into easier chunks. In the beginning he shows how to model something but then shows you details to build and lets you figure it out for yourself. I finished the modeling phase of his steam punk gun and just left off some details I felt were a little overkill. I am working on his spaceship now and everything is going smoothly. I never did want to model people and creatures so him not covering them is OK by me.
466949
The UML is not particularly _hard_ to learn--at least until you get into the deep magic of "metamodels" and suchlike, topics you can safely ignore pretty much forever.  But the UML is very, very large, and ecclectic, and it's easy to feel confused, lost and a little bit overwhelmed when confronting it for the first time.  Or the seventh.
The value of a little book like Alhir's lies in slicing out the "heart" of the UML and giving you an overview of the territory, so you can read most any diagram, write the ones that are most important to you, and you feel you understand the lay of the land.
This book has not exactly done a bad job of that task, but the simple truth is that it has a few key weaknesses, and I think you can do better elsewhere if you're looking for a first book to introduce you to the UML.
The first substantive chapter in the book, which introduces the concept of the UML as a language with "words," "sentences," "paragraphs," etc., is really quite good as an overview and orientation, and the quick look at each of the types of UML diagrams will leave you with a pretty good feel for what sorts of things you do with the UML.
The trouble begins when he starts tackling each of the diagrams in more detail in subsequent chapters.  I think it's fair to say that the narration plows into the mud somewhwere around page 70, as Alhir starts introuducing a veritable blur of diagram features: stereotypes, aggregations, dependencies, interfaces, and on and on.  Models are redrawn and redrawn, now with this feature, now with that feature, and it's hard enough to keep up with the parade of syntax, let alone divine why you would want to use one form or the other.
To _learn_ the UML you have to gain some idea of how the representations work together, showing different aspects of a system in different ways, and you have to learn what representations and what levels of detail are most suitable for different purposes.  And it's just that kind of guidance that seems to be lacking most in this book.  On one page, we have a class diagram with all the accessors, mutators, constructors and destructors written in the methods section, with visibility, parameters, etc.  On another, everything is abstracted out into package diagrams with interfaces and dependencies.  Remember that lost and confused feeling I was talking about?  There's where it started to set in for me.
Perhaps Mr. Alhir's shortcoming is that he has written this book after producing a couple of really nice _references_ on the UML (also from O'Reilly).  Too much of this book has a reference flavor: long on details, short on reasons.
Rather than _Learning UML_, I would recommend a book such as Fowler's _UML Distilled_ or Schmuller's _Teach Yourself UML in 24 Hours_ if you are brand-spanking-new to the UML.  I think that both of these books do a better job of picking out the most important aspects of UML and presenting them in a digestible manner.  But, that said, you are going to need more than one short book before you can claim to know the language, and I think you might find Alhir's book a really suitable _second_ step on the road to mastery.  You'll pick up a lot of details about the language and notation, and you'll find the extensive exercises (with solutions!) really valuable in increasing your skills.
3672933
Not only am I a visual learner but I learn by doing.  This book makes it so easy to jump in and start with its practical graphically oriented step by step lessons.  To drive the lessons home each chapter ends with practice exercises and each subsequent chapter builds on previous learning.  In addition, the book includes a summary HTML Tag Chart for that allows for quick reference.  Teaching yourself a new skill can be daunting especially when learning a new computer program from a complicated manual.  This is a great book for beginners making learning easy with out the challenge of having to decipher the instructions themselves.
3720919
Ever wonder how to compose a funky digital montage? Perhaps like those you see so often in magazines. Well, Photoshop has many ways for you to accomplish this, as demonstrated here. Other simpler books invariably show how you can include various image files into a Photoshop document and then do some elementary special effects. Trouble is, such efforts often yield crude results.

By contrast, artists in this book explain far more graceful techniques. Such that, collectively, a final image may reasonably be regarded as artwork in its own right.

The nine example chapters let you see different methods and together they demonstrate not a single correct way, but a spectrum of approaches. Which may encourage you to experiment. In this sense, the book is open ended, if you adopt this attitude.
1380880
Finally a book that doesn't skip parts or refer you to user's manual. Every step is presented in detail. I was surprised how much I learned about modeling from reading this book. This is the best max book I have read so far. Would love to see the authors do a advanced character modeling book.
4491156
Excellent book covering real examples of how people adapt to constraint and rules in their environment. I use it as a teaching tool regularly. Systeme-D rules.
4112497
Prof. Wagon shares his experience about the difficulties he encountered in ensuring the correctness of the algorithms.
The lessons he offers on the importance of careful thinking and meticulously checking the results of calculations is alone worth the price of the book.
The algorithms in the book require Mathematica 3. They seem to work well with Mathematica 4 as well.
The selection of topics is excellent. This book can provide a great start to a lot fun and learning with Mathematica.
I hope Prof. Wagon brings out a new expanded edition for the current edition of Mathematica soon.
3220612
Very good into the process of training.  Takes a while to get to the training parts.  Once, there the instructions are easy to understand and put together in a build block style.
922701
Vapnik and collaborators have developed the field of statistical learning theory underlying recent advances in machine learning and artificial intelligence (e.g. support vector machines). This book almost accomplishes  the formidable task of comprehensibly describing the essential ideas of  learning theory to non-statisticians. It contains ample theorems but almost  no proofs.
2572792
I wanted to find a book that could teach me how to use math to model problems better. This book covers many different ways to make mathematical models. This book isn't going to overwhelm you with math, it shows you the pitfalls with different ways of modelling things. I recommend this book to anyone who wants to learn how to apply math to real world problems.
4341216
My daughter wants to model and she really enjoy tips from this book. I hope it will help to take her farther in her modeling career.
1184934
Drawing from a photo as a reference is difficult when you're first learning how to draw, but not as difficult as trying to create a drawing on paper from just your imagination. The proportions inevitably end up imperfect, and your drawing itself comes out looking nothing like you imagined. That's why Freehand Figure Drawing for Illustrators is such a helpful book.

This book helps you to develop the skills to draw any figure without reference and create drawings that come out just as you imagined them. After some foundational instruction on perspective in drawing, the author, David H. Ross, walks readers through several types of figure drawings, giving them the reference points to draw any figure from imagination.

If you want to learn how to draw from memory, Freehand Figure Drawing for Illustrators will help you to develop the skills you need.

Review copy provided Blogging for Books
248223
This book is a good handbook for Bayesian practitioners to get a first-hand knowledge on the broad applications with Bayesian networks in different fields.

In this book, each application is illustrated with rich examples. However, it would be interesting for the next edition(if possible) to include examples of both learning and constructing the Bayesian networks. Currently, in some fields, learning part is missing.
1399621
Well illustrated and described poses.  Recommends sequences of varying degrees of difficulty.
3188688
A good book to sustain both unsupervised and supervised learning. I learned how the K-nearest neighbour works. Overall, this booklet is informational. There are lots of terminologies that are not familiar to me. It needs to enhance the explanation into basic English so that even the most novice reader can easily follow through. Overall, this is a good resource to read on machine learning.