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?

When performing sentiment analysis, we need to convert text to numbers to feed our deep neural network. To transform lists of vectorized sentences into arrays of the same size, we can apply the

K-fold cross-validation is a very useful method to reduce overfitting. It is especially useful when we have 

Which of the following is the pattern of organization for paragraph two?

On her fourth prenatal visit, a patient asks why urine tests are done on every visit. The most appropriate response is:

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?

Consider the code below num_epochs = 500 batch = 100 # Construct hidden layers inputs = tf.keras.layers.Dense(units=16, activation=’relu’, input_shape=[X_train.shape[1]]) hidden = tf.keras.layers.Dense(units=16, activation=’relu’) outputs = tf.keras.layers.Dense(units=1) # Stack the layers model = tf.keras.Sequential([inputs, hidden, outputs]) # Loss function and optimizer(with learning rate) loss = ‘mse’ optimizer = tf.keras.optimizers.Adam(0.001) # Compile the model model.compile(loss=loss, optimizer=optimizer, metrics=[‘mae’]) # Train the model history = model.fit(x_train_normalized, y_train_normalized, epochs=num_epochs, batch_size=batch, validation_split=0.1, verbose=0)After running the code above, how do you pick the new number of epochs to train your model one last time in the whole dataset?

Consider an economy with three possible states: bad, normal, and good. The probability of each state is given in the array of probabilities “p”  below. The payoff of a risky stock in each state is given in the array R.  p = np.array([0.1, 0.6, 0.3]) R = np.array([[0.05], [0.03], [-0.01]]) If we want to compute the expected return on this risky asset, what is the command line we should execute in Python?

Consider the code below with numbered lines:1)def h(x): 2) return np.exp(-x**2 / 2) / np.sqrt(2 * np.pi) 3) 4)x = np.linspace(-4, 4, 51) 5)y = np.zeros_like(x) 6) 7)for i in range(len(y)-1): 8) y[i] = h(x[i]) 9)plt.plot(x, y) If we run the code above, we will receive an error. In which line lies the error?

In an ML classification problem, before training the model for the last time,  we select the number of epochs that