This book provides the first accessible introduction to neural network analysis as a methodological strategy for social scientists. The author details numerous studies and examples which illustrate the advantages of neural network analysis over other quantitative and modelling methods in widespread use. Methods are presented in an accessible style for readers who do not have a background in computer science. The book provides a history of neural network methods, a substantial review of the literature, detailed applications, coverage of the most common alternative models and examples of two leading software packages for neural network analysis.
As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, ...
It is never too early to become a scientist With scientific and mathematical information from an expert, this is the perfect book for enlightening the next generation of geniuses.
This book covers both classical and modern models in deep learning.
Introduction to Neural Networks in Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence.
The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations making them especially useful in applications where the complexity of data or tasks makes the design of such ...
About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who ...
As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years.
Neural Networks and Learning Machines
... of the class Geos = { llow y [ cos ( w + x + b ) – cos ( b ) ] | 101 < rC ; } ( with pdf \ f ( w ) || CW || / C ) . ... Our class of approximating functions is the convex hull of G1 = { B ! ( a + b + x ) | IBI < 2rCf } and each ...
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