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Computer Science and Intelligent Systems

Computer Science and Intelligent Systems

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Foundations of Neural Computation

Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computationcollects, by topic, the most significant papers that have appeared in the journal over the past nine years.This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs.

Principles and Best Practice

This book addresses an often-neglected aspect of the creation of VHDL designs. A VHDL description is also source code, and VHDL designers can use the best practices of software development to write high-quality code and to organize it in a design. This book presents this unique set of skills, teaching VHDL designers of all experience levels how to apply the best design principles and coding practices from the software world to the world of hardware.

Habitual New Media

New media—we are told—exist at the bleeding edge of obsolescence. We thus forever try to catch up, updating to remain the same. Meanwhile, analytic, creative, and commercial efforts focus exclusively on the next big thing: figuring out what will spread and who will spread it the fastest. But what do we miss in this constant push to the future? In Updating to Remain the Same, Wendy Hui Kyong Chun suggests another approach, arguing that our media matter most when they seem not to matter at all—when they have moved from “new” to habitual.

The Birth of Computer Science

In 1936, when he was just twenty-four years old, Alan Turing wrote a remarkable paper in which he outlined the theory of computation, laying out the ideas that underlie all modern computers. This groundbreaking and powerful theory now forms the basis of computer science. In Turing’s Vision, Chris Bernhardt explains the theory, Turing’s most important contribution, for the general reader. Bernhardt argues that the strength of Turing’s theory is its simplicity, and that, explained in a straightforward manner, it is eminently understandable by the nonspecialist.

What Every Research Assistant Should Know

This book offers a practical guide to the computational methods at the heart of most modern quantitative research. It will be essential reading for research assistants needing hands-on experience; students entering PhD programs in business, economics, and other social or natural sciences; and those seeking quantitative jobs in industry. No background in computer science is assumed; a learner need only have a computer with access to the Internet.

A Study in Cognitive Science

To communicate, speakers need to make it clear what they are talking about. The act of referring, which anchors words to things, is a fundamental aspect of language. In this book, Kees van Deemter shows that computational models of reference offer attractive tools for capturing the complexity of referring.

This book presents the "great ideas" of computer science, condensing a large amount of complex material into a manageable, accessible form; it does so using the Java programming language. The book is based on the problem-oriented approach that has been so successful in traditional quantitative sciences.

A Gentle Introduction

In Great Ideas in Computer Science: A Gentle Introduction, Alan Biermann presents the "great ideas" of computer science that together comprise the heart of the field. He condenses a great deal of complex material into a manageable, accessible form. His treatment of programming, for example, presents only a few features of Pascal and restricts all programs to those constructions. Yet most of the important lessons in programming can be taught within these limitations.

This book introduces programming to readers with a background in the arts and humanities; there are no prerequisites, and no knowledge of computation is assumed. In it, Nick Montfort reveals programming to be not merely a technical exercise within given constraints but a tool for sketching, brainstorming, and inquiring about important topics. He emphasizes programming’s exploratory potential—its facility to create new kinds of artworks and to probe data for new ideas.

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