by Rolf Pfeifer, Dana Damian, Rudolf Fuchslin
Publisher: University of Zurich 2010
Number of pages: 109
Systematic introduction to neural networks, biological foundations; important network classes and learning algorithms; supervised models (perceptrons, adalines, multi-layer perceptrons), support-vector machines, echo-state networks, non-supervised networks (competitive, Kohonen, Hebb), recurrent networks (Hopfield, CTRNNs - continuous-time recurrent neural networks), spiking neural networks, spike-time dependent plasticity, applications.
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by Milan Hajek - University of KwaZulu-Natal
Contents: Introduction; Learning process; Perceptron; Back-propagation networks; The Hopfield network; Self-organizing feature maps; Temporal processing with neural networks; Radial-basis function networks; Adaline (Adaptive Linear System).
by Christian Dawson - MDPI AG
This Special Issue focuses on the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.
by Ben Goertzel - Plenum Press
This text applies the concepts of complexity science to provide an explanation of all aspects of human creativity. The book describes the model that integrates ideas from computer science, mathematics, neurobiology, philosophy, and psychology.
by Mark Watson - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).