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| for t in range(num_iterations): | |
| g = compute_gradient(x, y) | |
| m = beta_1 * m + (1 - beta_1) * g | |
| v = beta_2 * v + (1 - beta_2) * np.power(g, 2) | |
| v_hat = np.maximum(v, v_hat) | |
| w = w - step_size * m / (np.sqrt(v_hat) + epsilon) |
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