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Artificial neural networks are, as their name indicates, computational networks which attempt to simulate, in a gross manner, the networks of nerve cell (neurons) of the biological (human or animal) central nervous system. This simulation is a gross cell-by-cell (neuron-by-neuron, element-by-element) simulation. It borrows from the neurophysiological knowledge of biological neurons and of networks of such biological neurons. It thus differs from conventional (digital or analog) computing machines that serve to replace, enhance or speed-up human brain computation without regard to organization of the computing elements and of their networking. Still, we emphasize that the simulation afforded by neural networks is very gross. Why then should we view artificial neural networks (denoted below as neural networks or ANNs) as more than an exercise in simulation? We must ask this question especially since, computationally (at least), a conventional digital computer can do everything that an artificial neural network can do.
The answer lies in two aspects of major importance. The neural network, by its simulating a biological neural network, is in fact a novel computer architecture and a novel algorithmization architecture relative to conventional computers. It allows using very simple computational operations (additions, multiplication and fundamental logic elements) to solve complex, mathematically ill-defined problems, nonlinear problems or stochastic problems. A conventional algorithm will employ complex sets of equations, and will apply to only a given problem and exactly to it. The ANN will be (a) computationally and algorithmically very simple and (b) it will have a self-organizing feature to allow it to hold for a wide range of problems. |
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 Managing and Mining Graph Data (Advances in Database Systems)Managing and Mining Graph Data is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific... | | Algorithmic Adventures: From Knowledge to MagicThe public image of computer science does not reflect its true nature. The general public and especially high school students identify computer science with a computer driving license. They think that studying computer science is not a challenge, and that anybody can learn it. Computer science is not considered a scientific discipline but a... | | Graph-Theoretic Concepts in Computer Science: 35th International Workshop, WG 2009, Montpellier, France, June 24-26, 2009, Revised PapersThis book constitutes the thoroughly refereed post-conference proceedings of the 35th International Workshop on Graph-Theoretic Concepts in Computer Science, WG 2009, held in Montpellier, France, in June 2009.
The 28 revised full papers presented together with two invited papers were carefully reviewed and selected from 69 submissions.... |
Python Testing: Beginner's GuideAutomated testing moves much of the labor of testing off the developer and makes it easier as well as quicker to find bugs and fix them. Automated tests run faster, increase test coverage, and lower costs. However, testing is neither an easy process nor remotely exciting for most developers. But with the right techniques and tools, testing can... | | 97 Things Every Programmer Should Know: Collective Wisdom from the ExpertsProgrammers have a lot on their minds . Programming languages, programming techniques, development environments, coding style, tools, development process, deadlines, meetings, software architecture, design patterns, team dynamics, code, requirements, bugs, code quality. And more. A lot. There is an art, craft, and science to programming that... | | Algorithms for Next Generation Networks (Computer Communications and Networks)Data networking now plays a major role in everyday life and new applications continue to appear at a blinding pace. Yet we still do not have a sound foundation for designing, evaluating and managing these networks.
This book covers topics at the intersection of algorithms and networking. It builds a complete picture of the current state... |
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