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Things a Computer Scientist Rarely Talks about: God and Computers by Donald Ervin Knuth,

Things a Computer Scientist Rarely Talks about: God and Computers by Donald Ervin Knuth,
How does a computer scientist understand infinity? What can probability theory teach us about free will? Can mathematical notions be used to enhance one's personal understanding of the Bible? Perhaps no one is more qualified to address these questions than Donald E. Knuth, whose massive contributions to computing led others to nickname him "The Father of Computer Science" -- and whose religious faith led him to undertake a fascinating analysis of the Bible called the 3:16 project. In this series of six spirited, informal lectures, Knuth explores the relationship between his vocation and his faith, revealing the unique perspective that his work with computing has lent to his understanding of God. His starting point is the 3:16 project, an application of mathematical "random sampling" to the books of the Bible. The first lectures tell the story of the project's conception and execution, exploring the complex dimensions of language translation, aesthetics, and theological history. Along the way, something even more interesting is revealed: the many insights that Knuth gained from such interdisciplinary work. These theological musings culminate in a mindbending final lecture, which tackles infinity, free will, and the other Big Questions that lie at the juncture of theology and computation. Things a Computer Scientist Rarely Talks About, with its charming and user friendly format -- each lecture ends with a question and answer exchange, and the book itself contains more than 100 illustrations -- is the most readable, intriguing approach yet to this crucial topic. It is, quite simply, required reading, both for those who are serious -- yet curious -- about their faiths, and for thosewho look at the science of computation and wonder what it might teach them about their spiritual world.



Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence by Jyh-Shing Roger Jang,
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence by Jyh-Shing Roger Jang,
Neuro-Fuzzy and Soft Computing provides the first comprehensive treatment of the constituent methodologies underlying neuro-fuzzy and soft computing, an evolving branch of computational intelligence. The constituent methodologies include fuzzy set theory, neural networks, data clustering techniques, and several stochastic optimization methods that do not require gradient information. In particular, the authors put equal emphasis on theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. The book is well suited for use as a text for courses on computational intelligence and as a single reference source for this emerging field. To help readers understand the material the presentation includes more than 50 examples, more than 150 exercises, over 300 illustrations, and more than 150 Matlab scripts. In addition, Matlab is utilized to visualize the processes of fuzzy reasoning, neural-network learning, neuro-fuzzy integration and training, and gradient-free optimization (such as genetic algorithms, simulated annealing, random search, and downhill Simplex method). The presentation also makes use of SIMULINK for neuro-fuzzy control system simulations. All Matlab scripts used in the book are available on the free companion software disk that may be ordered by using the enclosed reply card. The book also contains an "Internet Resource Page" to point the reader to on-line neuro-fuzzy and soft computing home pages, publications, public-domain software, research institutes, news groups, etc. All the HTTP and FTP addresses are available as a bookmark file on the companion software disk.



Computer wallpaper - The terms wallpaper and desktop picture refer to an image used as a background on a computer screen, usually for the desktop of a graphical user interface. 'Wallpaper' is the term used in Microsoft Windows, while the Mac OS avoids mixing metaphors by calling it a 'desktop picture' (prior to Mac OS X, the term desktop pattern was used to refer to a small pattern that was repeated to fill the screen).

Free software movement - The free software movement began in 1983 when Richard Stallman announced the GNU project. The goal of the movement is to give freedom to computer users by replacing software which has restrictive licensing terms with free software (free as in freedom).

List of free Go programs - The following is a list of free (as in beer) computer programs for playing Go. Some are also free software.

Critical Computer Science - Critical computer science is the theory and praxis of software development which in some way questions its organization as labor and in other way rethinks the customary ways of thinking in software development. "Critical computer science" is a portmanteau term for phenomena which through theory or in practice depart from the economics of software development in the global free market.



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Computer Wallpaper - Computer Wallpaper Microsoft Windows XP Unleashed While what you create using your computer is a unique expression of who you are, how you use your computer probably isn`t. Nearly every Windows XP user follows the same Start menu path to launch programs, uses the same standard techniques in programs like Windows Explorer computer wallpaper and Outlook Express, computer wallpaper and performs the same customizations that don`t go much beyond changing the wallpaper. However, there are those of us who ...

Public Domain Software - Public Domain Software Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence by Jyh-Shing Roger Jang, Neuro-Fuzzy public domain software and Soft Computing provides the first comprehensive treatment of the constituent methodologies underlying neuro-fuzzy public domain software and soft computing, an evolving branch of computational intelligence. The constituent methodologies include fuzzy set theory, neural networks, data clustering techniques, public domain software and several stochastic optimization methods that do not require gradient information. In particular, ...

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