C: Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set.
Keras provides a weight regularization API that allows you to add a penalty for weight size to the loss function.
Three different regularizer instances are provided; they are:
L1: Sum of the absolute weights.
L2: Sum of the squared weights.
L1L2: Sum of the absolute and the squared weights.
A: Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either
"dropped out" of the net with probability 1-p or kept with probability p, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed.
By avoiding training all nodes on all training data, dropout decreases overfitting.
References:
https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight- regularization/
https://en.wikipedia.org/wiki/Convolutional_neural_network