Gradient Descent, Linear Regression, Machine Learning.
+ b) # Updating parameters m and b m -= lr * (-2 * Xs.dot(f).sum() / N) b -= lr * (-2 * f.sum() / N) log.append((m, b)) mse.append(mean_squared_error(y, m*X+b)) return m, b, log, mse We can observe the stochastic nature of the process. The regression line was jumping all over the place, trying to