Logistic Regression, Stochastic Gradient, Regularization.
vector of d-dims. Thus, the gradient simply multiplies each xi by the difference between the prediction and the target value. Since the weights are vector of d-dimensions, we scale each datapoint xi by the difference in prediction and target to update the w’s. Similarly, the derivative of the Loss