2022 Wu Enda machine learning specialization week 2 practice lab: linear expression

Exercise 1

# UNQ_C1
# GRADED FUNCTION: compute_cost

def compute_cost(x, y, w, b): 
    """ Computes the cost function for linear regression. Args: x (ndarray): Shape (m,) Input to the model (Population of cities) y (ndarray): Shape (m,) Label (Actual profits for the cities) w, b (scalar): Parameters of the model Returns total_cost (float): The cost of using w,b as the parameters for linear regression to fit the data points in x and y """
    # number of training examples
    m = x.shape[0] 
    
    # You need to return this variable correctly
    total_cost = 0
    
    ### START CODE HERE ### 
    
    total_cost = sum(pow((y-(x*w+b)),2))/(2*m)
    
    ### END CODE HERE ### 

    return total_cost

Exercise 2

# UNQ_C2
# GRADED FUNCTION: compute_gradient
def compute_gradient(x, y, w, b): 
    """ Computes the gradient for linear regression Args: x (ndarray): Shape (m,) Input to the model (Population of cities) y (ndarray): Shape (m,) Label (Actual profits for the cities) w, b (scalar): Parameters of the model Returns dj_dw (scalar): The gradient of the cost w.r.t. the parameters w dj_db (scalar): The gradient of the cost w.r.t. the parameter b """
    
    # Number of training examples
    m = x.shape[0]
    
    # You need to return the following variables correctly
    dj_dw = 0
    dj_db = 0
    
    ### START CODE HERE ### 
    dj_dw = sum(x*(x*w+b-y))/m
    dj_db = sum((x*w+b)-y)/m
    ### END CODE HERE ### 
        
    return dj_dw, dj_db

Video link ：https://www.bilibili.com/video/BV19B4y1W76i