Is there better way to maximize the speed

Course Q&A Machine Learning Specialization Supervised ML: Regression and Classification
1

This is from option labs for regularized cost function for logistic regression. I would like to ask in the first loop, Is there any optimization or way to neglect first for looping and use something from Numpy method to maximize efficiency of the code ?

def compute_cost_logistic_reg(X, y, w, b, lambda_ = 1):
    m,n  = X.shape
    cost = 0.
    
    for i in range(m):
        z_i = np.dot(X[i], w) + b                                      #(n,)(n,)=scalar, see np.dot
        f_wb_i = sigmoid(z_i)                                          #scalar
        cost +=  -y[i]*np.log(f_wb_i) - (1-y[i])*np.log(1-f_wb_i)      #scalar
    reg_cost = 0
    for j in range(n):
        reg_cost += (w[j]**2)                                          #scalar
    reg_cost = (lambda_/(2*m)) * reg_cost                              #scalar
    
    total_cost = cost + reg_cost                                       #scalar
    return total_cost                                                  #scalar
2

Any time you are iterating to compute the sum of some products, you can replace that entirely with a numpy dot product.

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