Which of the following options represents the transformation applied by a neuron to its inputs?

I was late and keep chatting with my classmates during class time,

If I want to make an audio or a video recording during class,

If I missed test 1, test 2 and my final exam score is 85,

Consider the function h = lambda x: x**2 + 4*x – 5 / x If we use fsolve from scipy.optimize to find the x where h(x) = 0, with a starting guess of x0=0.01, what is the output we obtain?

If I missed test 1 and my final exam score is 85,

Consider the pseudo code below to obtain the efficient 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() 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}) result = minimize(f, np.zeros(5), constraints=cons) w_optimal = result.x sd_optimal = 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?

What is the output of the following code? newlist = np.zeros(4) for i in range(len(newlist)):    newlist = i + newlist print(newlist)   

Consider a portfolio with the following weights w,  expected return on each risky asset ER, and covariance matrix Cov below. w = np.array() ER = np.array()Cov = np.cov(, ]) Which of the following expressions represents the portfolio volatility in Python?

How many atoms are in a face centered cubic lattice?