Abstract
Regularization is a technique for reducing the variance in the validation set, thus preventing the model from overfitting during training. In doing so, the model can better generalize to new examples. When training deep neural networks, a couple of strategies exist for use as a regularizer.
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© 2019 Ekaba Bisong
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Bisong, E. (2019). Regularization for Deep Learning. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4470-8_34
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DOI: https://doi.org/10.1007/978-1-4842-4470-8_34
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-4469-2
Online ISBN: 978-1-4842-4470-8
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