Gradient Descent, Linear Regression, Machine Learning.
b -= lr * (-2 * f.sum() / N) log.append((m, b)) mse.append(mean_squared_error(y, (m*X + b))) return m, b, log, mse We can track visually how the algorithm was approaching a local minimum. To the naked eye, it seems like it didn’t converge completely. To solve that we can increase epochs and