Stochastic Gradient, Machine Learning, AI, Gradient Descent.
data point: xˆ^i is w^T xˆ^(i) = ∑ wⱼ (x^i)ʲ . We can interpret this as a nonlinear prediction for the original data point x^i . Overfitting Computational resources permitting, more data is always better. What about more features always better? The benefit is intuitively clear — better accuracy of