If we include a callback to early stop our training process…
If we include a callback to early stop our training process and prevent overfitting if we do not observe progress in 10 epochs like the one below callback_list = [tf.keras.callbacks.EarlyStopping(monitor=’mae’, patience=10)] where should we include it in our ML architeture?
Read DetailsConsider a portfolio with the following weights w, expected…
Consider a portfolio with the following weights w, expected return on each risky asset ER, and covariance matrix Cov below. w = np.array([0.05, 0.03]) ER = np.array([0.10, 0.02])Cov = np.cov([[0.004, 0.0156], [0.0156, 0.009]]) Which of the following expressions represents the portfolio variance in Python?
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