Frequentist statistics says that
there exists some true underlying parameters for a model. While Bayesian statistics says that
there are different beliefs about parameters for a model.
In backpropgation, we essentially throw data at a neural network, backpropagate the gradients error, and hope that the network learns the true underlying parameters.
Is training a neural network model with backpropagation, a frequentist approach?