2009, ISBN: 9788120313514

Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, Stony Brook, New York, 1973. First Edition. Library Buckram. Good Condition (ex-library)/No Dus… More...

Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, Stony Brook, New York, 1973. First Edition. Library Buckram. Good Condition (ex-library)/No Dust Jacket. With illustrations. Original wrappers bound in. Slight wear & slight soiling to spine, covers & corners. Size: Quarto. Quantity Available: 1. Shipped Weight: 1-2 kilos. Category: Science, Mathematics & Technology; Biology, Natural History & Botany; United States; 1970s; Academic Periodicals & Magazines. Pictures of this item not already displayed here available upon request. Inventory No: 015098. ., Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, 1973, Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, Stony Brook, New York, 1975. First Edition. Library Buckram. Good Condition (ex-library)/No Dust Jacket. With illustrations. Original wrappers bound in. Slight wear & slight soiling to spine, covers & corners. Size: Quarto. Quantity Available: 1. Shipped Weight: 1-2 kilos. Category: Science, Mathematics & Technology; Biology, Natural History & Botany; United States; 1970s; Academic Periodicals & Magazines. Pictures of this item not already displayed here available upon request. Inventory No: 015100. ., Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, 1975, Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, Stony Brook, New York, 1972. First Edition. Library Buckram. Good Condition (ex-library)/No Dust Jacket. With illustrations. Original wrappers bound in. Slight wear & slight soiling to spine, covers & corners. Size: Quarto. Quantity Available: 1. Shipped Weight: 1-2 kilos. Category: Science, Mathematics & Technology; Biology, Natural History & Botany; United States; 1970s; Academic Periodicals & Magazines. Pictures of this item not already displayed here available upon request. Inventory No: 015097. ., Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, 1972, Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, Stony Brook, New York, 1976. First Edition. Library Buckram. Good Condition (ex-library)/No Dust Jacket. With illustrations. Original wrappers bound in. Slight wear & slight soiling to spine, covers & corners. Slight staining to back cover. Upper joint loosening slightly. Size: Quarto. Quantity Available: 1. Shipped Weight: 1-2 kilos. Category: Science, Mathematics & Technology; Biology, Natural History & Botany; United States; 1970s; Academic Periodicals & Magazines. Pictures of this item not already displayed here available upon request. Inventory No: 015144. ., Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, 1976, Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, Stony Brook, New York, 1970. First Edition. Library Buckram. Good Condition (ex-library)/No Dust Jacket. With b/w & coloured plates, illustrations. Original wrappers bound in. Slight wear to spine, covers & corners. Size: Quarto. Quantity Available: 1. Shipped Weight: 1-2 kilos. Category: Science, Mathematics & Technology; Biology, Natural History & Botany; United States; 1970s; Academic Periodicals & Magazines. Pictures of this item not already displayed here available upon request. Inventory No: 015095. ., Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, 1970, Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, Stony Brook, New York, 1985. First Edition. Library Buckram. Good Condition (ex-library)/No Dust Jacket. With illustrations.. Slight wear to spine, covers & corners. Size: Quarto. Quantity Available: 1. Shipped Weight: 1-2 kilos. Category: Science, Mathematics & Technology; Biology, Natural History & Botany; United States; 1980s; Academic Periodicals & Magazines. Pictures of this item not already displayed here available upon request. Inventory No: 015092. ., Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, 1985, Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, Stony Brook, New York, 1980. First Edition. Library Buckram. Good Condition (ex-library)/No Dust Jacket. With illustrations. Original wrappers bound-in. Slight wear & slight soiling to spine, covers & corners. Size: Quarto. Quantity Available: 1. Shipped Weight: 1-2 kilos. Category: Science, Mathematics & Technology; Biology, Natural History & Botany; United States; 1980s; Academic Periodicals & Magazines. Pictures of this item not already displayed here available upon request. Inventory No: 015151. ., Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, 1980, Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, Stony Brook, New York, 1977. First Edition. Library Buckram. Good Condition (ex-library)/No Dust Jacket. With illustrations. Original wrappers bound in. Slight wear & slight soiling to spine, covers & corners. Front cover somewhat stained. Size: Quarto. Quantity Available: 1. Shipped Weight: 1-2 kilos. Category: Science, Mathematics & Technology; Biology, Natural History & Botany; United States; 1970s; Academic Periodicals & Magazines. Pictures of this item not already displayed here available upon request. Inventory No: 015147. ., Published by Stony Brook Foundation, Inc. for State University of New York at Stony Brook, 1977, Dover Publications. Paperback. New. Paperback. 256 pages. Dimensions: 8.3in. x 5.8in. x 0.7in.Noteworthy for the philosophical subtlety of its foundations and the elegance of its problem-solving methods, statistical mechanics can be employed in a broad range of applications among them, astrophysics, biology, chemistry, nuclear and solid state physics, communications engineering, metallurgy, and mathematics. Geared toward graduate students in physics, this text covers such important topics as stochastic processes and transport theory in order to provide students with a working knowledge of statistical mechanics. To explain the fundamentals of his subject, the author uses the method of ensembles developed by J. Willard Gibbs. Topics include the properties of the Fermi-Dirac and Bose-Einstein distributions; the interrelated subjects of fluctuations, thermal noise, and Brownian movement; and the thermodynamics of irreversible processes. Negative temperature, magnetic energy, density matrix methods, and the Kramers-Kronig causality relations are treated briefly. Most sections include illustrative problems. Appendix. 28 figures. 1 table. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN., Dover Publications, PHI Learning, 2009. First edition. Softcover. New. 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. CONTENTS: Introduction. Acknowledgments. Properties of Single Neurons. Synaptic Integration and Neuron Models. Essential Vector Operations. Lateral Inhibition and Sensory Processing. Simple Matrix Operations. The Linear Associator: Background and Foundations. The Linear Associator: Simulations. Early Network Models: The Perceptron. Gradient Descent Algorithms. Represen-tation of Information. Applications of Simple Associators: Concept Formation and Object Motion. Energy and Neural Networks: Hopfield Networks and Boltzmann. Machines. Nearest Neighbor Models. Adaptive Maps. The BSB Model: A Simple Nonlinear Autoassociative Neural Network. Associative Computation. Teaching Arithmetic to a Neural Network. Afterword. Index. Printed Pages: 668., PHI Learning, 2009<

2009, ISBN: 9788120313514

PHI Learning, 2009. First edition. Softcover. New. An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more th… More...

PHI Learning, 2009. First edition. Softcover. New. 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. CONTENTS: Introduction. Acknowledgments. Properties of Single Neurons. Synaptic Integration and Neuron Models. Essential Vector Operations. Lateral Inhibition and Sensory Processing. Simple Matrix Operations. The Linear Associator: Background and Foundations. The Linear Associator: Simulations. Early Network Models: The Perceptron. Gradient Descent Algorithms. Represen-tation of Information. Applications of Simple Associators: Concept Formation and Object Motion. Energy and Neural Networks: Hopfield Networks and Boltzmann. Machines. Nearest Neighbor Models. Adaptive Maps. The BSB Model: A Simple Nonlinear Autoassociative Neural Network. Associative Computation. Teaching Arithmetic to a Neural Network. Afterword. Index. Printed Pages: 668. NA, PHI Learning, 2009<

2009, ISBN: 9788120313514

PHI Learning, 2009. First edition. Softcover. New. An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more th… More...

PHI Learning, 2009. First edition. Softcover. New. 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. CONTENTS: Introduction. Acknowledgments. Properties of Single Neurons. Synaptic Integration and Neuron Models. Essential Vector Operations. Lateral Inhibition and Sensory Processing. Simple Matrix Operations. The Linear Associator: Background and Foundations. The Linear Associator: Simulations. Early Network Models: The Perceptron. Gradient Descent Algorithms. Represen-tation of Information. Applications of Simple Associators: Concept Formation and Object Motion. Energy and Neural Networks: Hopfield Networks and Boltzmann. Machines. Nearest Neighbor Models. Adaptive Maps. The BSB Model: A Simple Nonlinear Autoassociative Neural Network. Associative Computation. Teaching Arithmetic to a Neural Network. Afterword. Index. Printed Pages: 668., PHI Learning, 2009<

2009, ISBN: 9788120313514

PHI Learning, 2009. First edition. Softcover. New. An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more th… More...

PHI Learning, 2009. First edition. Softcover. New. 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. CONTENTS: Introduction. Acknowledgments. Properties of Single Neurons. Synaptic Integration and Neuron Models. Essential Vector Operations. Lateral Inhibition and Sensory Processing. Simple Matrix Operations. The Linear Associator: Background and Foundations. The Linear Associator: Simulations. Early Network Models: The Perceptron. Gradient Descent Algorithms. Represen-tation of Information. Applications of Simple Associators: Concept Formation and Object Motion. Energy and Neural Networks: Hopfield Networks and Boltzmann. Machines. Nearest Neighbor Models. Adaptive Maps. The BSB Model: A Simple Nonlinear Autoassociative Neural Network. Associative Computation. Teaching Arithmetic to a Neural Network. Afterword. Index. Printed Pages: 668., PHI Learning, 2009<

2009, ISBN: 9788120313514

PHI Learning, 2009. First edition. Softcover. New. An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more th… More...

PHI Learning, 2009. First edition. Softcover. New. 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. CONTENTS: Introduction. Acknowledgments. Properties of Single Neurons. Synaptic Integration and Neuron Models. Essential Vector Operations. Lateral Inhibition and Sensory Processing. Simple Matrix Operations. The Linear Associator: Background and Foundations. The Linear Associator: Simulations. Early Network Models: The Perceptron. Gradient Descent Algorithms. Represen-tation of Information. Applications of Simple Associators: Concept Formation and Object Motion. Energy and Neural Networks: Hopfield Networks and Boltzmann. Machines. Nearest Neighbor Models. Adaptive Maps. The BSB Model: A Simple Nonlinear Autoassociative Neural Network. Associative Computation. Teaching Arithmetic to a Neural Network. Afterword. Index. Printed Pages: 668., PHI Learning, 2009<