Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners

© Ekaba Bisong 2019

E. . BisongBuilding Machine Learning and Deep Learning Models on Google Cloud Platformhttps://doi.org/10.1007/978-1-4842-4470-8_34

34. Regularization for Deep Learning

Ekaba Bisong¹ 

(1)

OTTAWA, ON, Canada

 

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.

Dropout

Dropout is a regularization technique that prevents a deep neural network from overfitting by randomly discarding a number of neurons at every layer during training. In doing so, the neural network is not overly dominated by any one ...