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Jez Humble, David Farley
The step-by-step guide to going live with new software releases faster - reducing risk and delivering more value sooner! * *Fast, simple, repeatable techniques for deploying working code to production in hours or days, not months! *Crafting custom processes that get developers from idea to value faster than ever. *Best practices for everything from source code control to dependency management and in-production tracing. *Common obstacles to rapid release - and pragmatic solutions. In too many organizations, build, testing, and deployment processes can take six months or more. That's simply far too long for today's businesses. But it doesn't have to be that way. It's possible to deploy working code to production in hours or days after development work is complete - and Go Live presents comprehensive processes and techniques for doing so. Written by two of the world's most experienced software project leaders, this book demonstrates how to dramatically increase speed while reducing risk and improving code quality at the same time. The authors cover all facets of build, testing, and deployment, including: configuration management, source code control, release planning, auditing, compliance, integration, build automation, and more. They introduce a wide range of advanced techniques, including inproduction monitoring and tracing, dependency management, and the effective use of virtualization. For each area, they explain the issues, show how to mitigate the risks, and present best practices. Throughout, Go Live focuses on powerful opportunities for individual improvement, clearly and simply explaining skills and techniques so they can be used every day on real projects. With this book's help, any development organization can move from idea to release faster -- and deliver far more value, far more rapidly.
Didier H. Besset
Numerical methods naturally lend themselves to an object-oriented approach. Mathematics builds high- level ideas on top of previously described, simpler ones. Once a property is demonstrated for a given concept, it can be applied to any new concept sharing the same premise as the original one, similar to the ideas of reuse and inheritance in object-oriented (OO) methodology. Few books on numerical methods teach developers much about designing and building good code. Good computing routines are problem-specific. Insight and understanding are what is needed, rather than just recipes and black box routines. Developers need the ability to construct new programs for different applications. Object-Oriented Implementation of Numerical Methods reveals a complete OO design methodology in a clear and systematic way. Each method is presented in a consistent format, beginning with a short explanation and following with a description of the general OO architecture for the algorithm. Next, the code implementations are discussed and presented along with real-world examples that the author, an experienced software engineer, has used in a variety of commercial applications. On the enclosed CD-ROM, you'll find files containing tested source code implementations of all the algorithms discussed in the book in both Java and Smalltalk. This includes repository files for VisualAge for Java and VisualAge for Smalltalk under ENVY. * Reveals the design methodology behind the code, including design patterns where appropriate, rather than just presenting canned solutions. * Implements all methods side by side in both Java and Smalltalk. This contrast can significantly enhance your understanding of the nature of OO programming languages. * Provides a step-by-step pathway to new object-oriented techniques for programmers familiar with using procedural languages such as C or Fortran for numerical methods. * Includes a chapter on data mining, a key application of numerical methods.
John K. Kruschke
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks--t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus. Free software now includes programs in JAGS, which runs on Macintosh, Linux, and Windows. Author website: http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/ -Accessible, including the basics of essential concepts of probability and random sampling -Examples with R programming language and BUGS software -Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). -Coverage of experiment planning -R and BUGS computer programming code on website -Exercises have explicit purposes and guidelines for accomplishment
A step-by-step manual discusses the best ways to enhance a Web site with Ajax so that information on a site can be updated without refreshing the entire page, explaining how developers that use CSS and (X)HTML can build Ajax functionality without frameworks and use progressive enhancement techniques to ensure that sites are usable in all browsers. Original. (Intermediate/Advanced)