Machine Learning, Neural Networks, Statistics, Data Science, AI.
universal approximation theorem). When we are using Stochastic Gradient Descent (SGD) to fit our network’s parameters to the learning problem at hand, we take, at each iteration of the algorithm, a step in the solution space towards the gradient of the loss function J(θ; X, y) in respect to the