Which of these is not an opening in the oropharynx?

The major final product that results from the digestion of carbohydrates is:

Consider the numbered lines below:1)def price(PMT, IY, g) 2) return PMT / (IY – g) 3) 4)g = np.linspace(0, 0.09, 21) 5)IY = 0.1 6)PMT = 10 7)PV = np.zeros_like(g) 8)for i in range(len(g)): 9) PV[i] = price(PMT, IY, g[i]) 10)plt.plot(g, PV) 11)plt.xlabel(‘Growth Rate’) 12)plt.ylabel(‘Perpetuity with Growth Price’);If we execute the code above, we receive an error. In which line lies the error?

The term used to describe the volume of air exchanged during normal inspiration and expiration is:

Assume we download the stock price of Tesla and compute its return using the command  startdate = ‘2019-01-01’ enddate = ‘2021-01-01’ tesla = web.get_data_yahoo(“TSLA”, startdate, enddate)R_tesla = tesla[‘Adj Close’].pct_change().dropna() Which of the following commands is not a valid command in Python?  

Adam and SGD are examples of

Consider the following code with numbered lines: 1)betai = np.array([-2, -1.5, -1, -0.5, 0., 0.5, 1, 1.5, 2]) # Features 2)ERi = np.array([-0.08, -0.06, -0.03, -0.01, 0.02, 0.04, 0.07, 0.1 , 0.12]) # Labels 3)hidden = tf.keras.layers.Dense(units=1, input_shape=[1]) 4)model = tf.keras.Sequential([hidden]) 5)loss = ‘mse’ 6)optimizer = ‘Adam’ 7)model.compile(loss=loss, optimizer=optimizer) 8)history = model.fit(betai, ERi, epochs=10000, verbose=False) 9)plt.plot(history.history[‘loss’]) 10)plt.xlabel(‘Number of Epochs’) 11)plt.ylabel(‘Loss’);In which of line does the training of the neural network takes place?

Sentence nine is a/n

Suppose we consider a one-step binomial tree model to price a derivative that pays in the up state and in the down state.  What is the price of this derivative?

Consider the pseudo code below to obtain the effient portfolios:from scipy.optimize import minimize f = lambda w: TO BE FILLED mu = np.linspace(15, 30, 31) sd_optimal = np.zeros_like(mu) w_optimal = np.zeros([31, 5]) for i in range(len(mu)): # Optimization Constraints cons = ({‘type’:’eq’, ‘fun’: lambda w: np.sum(w) – 1}, {‘type’:’eq’, ‘fun’: lambda w: w @ ER * 252 * 100 – mu[i]}) result = minimize(f, np.zeros(5), constraints=cons) w_optimal[i, :] = result.x sd_optimal[i] = np.sqrt(result.fun)Assuming that ER are Cov given, what should we substitute TO BE FILLED for in order to get the desired result?