Hello World
Linear Regression MVP Here is a quick example of a math formula and a Python snippet working together. Objective Function We define our mean squared error cost function as: $$J(\theta) = \frac{1}{2m} \sum_{i=1}^{m} (h_\theta(x^{(i)}) - y^{(i)})^2$$Python Code import numpy as np def calculate_mse(y_true: np.ndarray, y_pred: np.ndarray) -> float: """Computes Mean Squared Error.""" return np.mean((y_true - y_pred) ** 2) # Quick Test y_true = np.array([1.0, 2.0, 3.0]) y_pred = np.array([1.1, 1.9, 3.2]) print(f"MSE: {calculate_mse(y_true, y_pred):.4f}")