Combined Regularisation Techniques for Artificial Neural Networks
Artificial neural networks are prone to overfitting – the process of learning details specific to a particular training data set. Success in preventing overfitting through combining the L2 and dropout regularisation techniques has led to the combination’s recent popularity. However, with the introduction of each additional regularisation technique to an artificial neural network, there comes new h
