Machine Learning, Gradient Descent, Stochastic Gradient, Data Science, Learning Curve.
iterations w = 0 # Model parameter # Perform Gradient Descent for epoch in range(epochs): y_pred = w * X gradient = (-2/len(X)) * sum(X * (y - y_pred)) w = w - alpha * gradient print("Optimal parameter is: w =", w) Step Size choice The learning rate α is a crucial hyperparameter in Gradient