The ML architecture used here is a deep feed-forward neural network (NN) that consists of several layers of densely connected neurons, 3 see Fig. . For each neuron, the NN applies a nonlinear activation function 𝜑 𝑘 to a weighted linear combination of the previous layer's output to create a complex network of linear and nonlinear functions. In a dense layer, the 𝑗th component of the 𝑘th layer 𝑦 𝑗,𝑘 is defined in terms of inputs from the previous layer 𝑦 𝑖,𝑘-1 , weight parameters 𝑤 𝑖,𝑗,𝑘 , and bias parameters 𝑐 𝑗,𝑘 such that