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Gradient descent for linear regression

 import math, copy import numpy as np import matplotlib.pyplot as plt plt.style.use('./deeplearning.mplstyle') from lab_utils_uni import plt_house_x, plt_contour_wgrad, plt_divergence, plt_gradients # Load our data set x_train = np.array([1.0, 2.0])   #features y_train = np.array([300.0, 500.0])   #target value #Function to calculate the cost 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 compute_gradient(x, y, w, b):      """     Computes the gradient for linear regression      Args:       x (ndarray (m,)): Data, m examples        y (ndarray (m,)): target values       w,b (scalar)...
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