An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and ...
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An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas. Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject. The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.
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Add this copy of An Introduction to Neural Networks to cart. $11.00, very good condition, Sold by HPB-Diamond rated 4.0 out of 5 stars, ships from Dallas, TX, UNITED STATES, published 1995 by Bradford Books.
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Add this copy of An Introduction to Neural Networks to cart. $17.40, fair condition, Sold by ThriftBooks-Dallas rated 5.0 out of 5 stars, ships from Dallas, TX, UNITED STATES, published 1995 by Bradford Book.
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Very Good in Printed Boards jacket. 4to-over 9¾"-12" tall. 650 pp. Tightly bound. Corners not bumped. Text is free of markings. No ownership markings. NOTE: Foxing top fore-edge.
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Very Good + in None jacket. Clean, solid copy with previous owner's stamp on inside front cover. Text is unmarked. Binding is solid. Cover is glossy with mild surface wear. Cover corners are square. Pressure bend on bottom of spine. Appears to have had little use. Books, box sets, and items other than standard jewel case CDs and DVDs that sell for $9 or more ship in a box; under $9 in a bubble mailer. Expedited and international orders may ship in a flat rate envelope rather than a box due to cost constraints. All US-addressed items ship with complimentary delivery confirmation.
Add this copy of An Introduction to Neural Networks to cart. $49.72, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Newport Coast, CA, UNITED STATES, published 1995 by Bradford Books.
