Machine Learning, Gradient Descent, Stochastic Gradient, Batch Gradient Descent, Regression.
x2). For SGD, you consider only one data sample at a time. Which means, Csgd(W, X) = (w0 + w1*xi — y)², for a random xi. Now try to understand, we apply stochastic gradient descent and update W in such a way that Csgd reduces. What will this do? It will make the hypothesis lean towards this data