Stochastic Gradient, Machine Learning, AI, Gradient Descent.
3, 9, 81, 243). Now imagine solving the linear regression problem not with the original data points x¹, . . . , xᵐ , but with their expanded versions xˆ¹, . . . , xˆᵐ. The result is a linear function in d+1 dimensions, specified by coefficients w₀ , . . . , wₙ The prediction of w for an expanded