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