Logistic Regression, Stochastic Gradient, Regularization.
1, wx = 10000 * 13, σ(wx) ≈ 1, and thus we get log(1) = 0. As w gets larger and larger, wx gets to ± ∞, and thus L gets to 0. Since, the graident of the Loss w.r.t w is a scaled version of each point xi, this would also allow variables with larger values to dominate if we do not have scaled data,