Question about Week 1 Lab 4

Course Q&A Machine Learning Specialization Supervised ML: Regression and Classification
1 
def compute_cost(x, y, w, b):
   
    m = x.shape[0] 
    cost = 0
    
    for i in range(m):
        f_wb = w * x[i] + b
        cost = cost + (f_wb - y[i])**2
    total_cost = 1 / (2 * m) * cost

    return total_cost

def gradient_descent(x, y, w_in, b_in, alpha, num_iters, **cost_function**, gradient_function): 
    """
    Performs gradient descent to fit w,b. Updates w,b by taking 
    num_iters gradient steps with learning rate alpha
    """
    then continue...

“cost_function” and “gradient_function” are functions in the code, I am not sure why we do not need to add arguments with parenthesis after the function name “cost_function”?

2

This is the ungraded lab that is why you don’t need to add anything. Input arguments are already given to both functions.

4

Thank you for your reply!