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Product: Book - Paperback
Title: Microsoft Visual Basic 6.0 Professional Step-By- Step.
Publisher: Microsoft Press
Authors: Michael Halvorson
Rating: 5/5
Customer opinion - 5 stars out of 5
Great book for VB Newbies


I never thought VB could be so easy. I only knew HTML and now Ialso now VB. Ya, it took me two months. But, I got it all down now anduse it at work. This book has made me into a happy little computer geek. ...Just pace yourself and this book will teach you the basic's and you'll be all the happier for it.



Product: Book - Paperback
Title: The 22 Immutable Laws of Branding
Publisher: HarperBusiness
Authors: Al Ries, Laura Ries
Rating: 5/5
Customer opinion - 5 stars out of 5
A "must-read" for anyone in business


This book should be considered compulsary reading for everyone in management ; from the chairman of the board down. This applies equally to those who are teaching business management. In 22 short, easy-to-understand and easy-to-read chapters, the authors explain in simple language, that which most of us would hope to have learnt after as many years in business.



Product: Book - Paperback
Title: Moving to VB .NET: Strategies, Concepts, and Code
Publisher: Apress
Authors: Daniel Appleman, Dan Appleman
Rating: 5/5
Customer opinion - 5 stars out of 5
excellent book


Very well written. I don't recommend this book to someone who is new to VB or programming. Very well written and actually engrossing, as compared to the many other dry tech manuals out there. I've read other books on the .net platform (from ms press and wrox) and found this to be the best of them so far.



Product: Book - Hardcover
Title: The Elements of Statistical Learning
Publisher: Springer
Authors: T. Hastie, R. Tibshirani, J. H. Friedman
Rating: 5/5
Customer opinion - 5 stars out of 5
One of the Essential Books on Modern Machine Learning


This book is a miracle of clarity and comprehensiveness. It presents a unified approach to state of the art machine learning techniques from a statistical perspective. The layout is logical and the level of math is appropriate for applications-oriented engineers and computer scientists, as well as theorists. Sections where the book does need to go into heavier mathematics are clearly marked and generally optional. I found the book very easy to read, but at the same time very comprehensive.
The book provides a very illuminating counterpoint to other books that promote the Computational Learning Theory (COLT / kernels / large margins) viewpoint of modern machine learning. Many of the same techniques such as boosting and support vector machines are discussed, but are motivated in different ways. Appropriate regularization is seen as the key to avoiding overfitting with complex models, rather than margin maximization. Mathematically you often end up solving the same problem, but personally I usually find the statistical approach much more direct and intuitive.
This book is a nice follow on to introductory pattern recognition texts such as Duda and Hart, though it can be read without any prior pattern recognition knowledge. It provides a nice mix of theory and paractical algorithms, illustrated with numerous examples. An essential element of your machine learning library!