I have learnt from some examples the existence of regularization option at ANNs (concretely, at Keras implementation). As far as I know, regularization in general is a kind of "penalty" on parameters to prevent model complexity and overfitting.
b_regularizer options in Keras are for weight and bias parameter regularization, unless I am mistaken. But what is
activity_regularizer for? How is it related to the weight/bias regularization? And more generally: what is a good practice to using all these regularization possibilites (apart from the blind brute force tuning)? Because of ANNs/CNNs are produce very low overfitting measured on the validation set, it seems me that regularization is not a really useful tool with neural nets.