Introduction
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An introduction to the chapter, including information on additional resources
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Architecture
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A discussion of the architecture, simulation, and training of backpropagation networks
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Faster Training
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A discussion of several high-performance backpropagation training algorithms
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Speed and Memory Comparison
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A comparison of the memory and speed of different backpropagation training algorithms
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Improving Generalization
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A discussion of two methods for improving generalization of a network -- early stopping and regularization
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Preprocessing and Postprocessing
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A discussion of preprocessing routines that can be used to make training more efficient, along with techniques to measure the performance of a trained network
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Sample Training Session
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A tutorial consisting of a sample training session that demonstrates many of the chapter concepts
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Limitations and Cautions
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A discussion of limitations and cautions to consider when creating and training perceptron networks
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