Abstract
The more commonly used Artificial Neural Network models are first characterized. These characteristics — training parameters and the like — are related to high-level language constructs (C/C++). The necessity of Graphical User Interfaces, from an educational perspective, is highlighted. Experiences are then recounted gained from a decade of teaching a graduate-level course on ANNs. Representative public domain and commercial ANN software simulators are covered (some of the former types accompanying ANN textbooks). Particular emphasis is placed on BackPropagation/Multi-Layered Perceptrons using NeuralWare software.
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Fulcher, J. (2000). Teaching Course on Artificial Neural Networks. In: Jain, L.C. (eds) Innovative Teaching and Learning. Studies in Fuzziness and Soft Computing, vol 36. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1868-0_7
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DOI: https://doi.org/10.1007/978-3-7908-1868-0_7
Publisher Name: Physica, Heidelberg
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