Description
The confluence of cloud computing, parallelism and advancedmachine intelligence approaches has created a world in which theoptimum knowledge system will usually be architected from thecombination of two or more knowledge-generating systems. There is aneed, then, to provide a reusable, broadly-applicable set of designpatterns to empower the intelligent system architect to takeadvantage of this opportunity.This book explains how to design and build intelligent systemsthat are optimized for changing system requirements (adaptability),optimized for changing system input (robustness), and optimized forone or more other important system parameters (e.g.,accuracy, efficiency, cost). It provides an overview of traditionalparallel processing which is shown to consist primarily of task andcomponent parallelism; before introducing meta-algorithmicparallelism which is based on combining two or more algorithms,classification engines or other systems.Key features:
- Explains the entire roadmap for the design, testing,development, refinement, deployment and statistics-drivenoptimization of building systems for intelligence
- Offers an accessible yet thorough overview of machineintelligence, in addition to having a strong image processingfocus
- Contains design patterns for parallelism, especiallymeta-algorithmic parallelism simply conveyed, reusable andproven effective that can be readily included in the toolbox ofexperts in analytics, system architecture, big data, security andmany other science and engineering disciplines
- Connects algorithms and analytics to parallelism, therebyillustrating a new way of designing intelligent systems compatiblewith the tremendous changes in the computing world over the pastdecade
- Discusses application of the approaches to a wide number offields; primarily, document understanding, image understanding,biometrics and security printing
- Companion website contains sample code and data sets
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