Welcome to IOU Digital Library

This manuscript offers an accessible yet thorough introduction to artificial neural networks (ANNs), starting from biological inspiration and history, then formalizing components of ANNs (neurons, activation, network topologies), and covering a wide range of learning paradigms — supervised (perceptrons, multilayer perceptrons, radial-basis functions, backpropagation), recurrent networks and Hopfield networks; and unsupervised paradigms such as self-organizing maps and adaptive-resonance theory. It also includes appendices on clustering, online learning, reinforcement learning, and practical considerations — useful both for beginners and more advanced readers interested in a broad overview of neural-network theory and techniques.

no reviews

245: $a: A Brief Introduction to Neural Networks
245: $b:
082: $a:
100: $a: David Kriesel
650: $a: Machine Learning
250: $a:
440: $v:
250: $b: Self-publishing
250: $c: 2007
020: $a:
300: $a: 244