Stochastic Gradient, Machine Learning, AI, Gradient Descent.
a trade-off between expressivity and predictive power. To understand the issue, let’s return to the case of n = 1 and the map x → xˆ = (1, x, x² , . . . , xᵈ ). Suppose we take d = m, so that we are free to use polynomials with degree equal to the number of data points. Now we can get a