Multi-layer perceptron. Consider the following neural network defined in PyTorch.  class NeuralNetwork(nn.Module):    def __init__(self):        super().__init__()        self.linear_relu_stack = nn.Sequential(            nn.Linear(20, 100),            nn.ReLU(),            nn.Linear(100, 100),            nn.ReLU(),            nn.Linear(100, 3),        )    def forward(self, x):        logits = self.linear_relu_stack(x)        return logits (a) (2 pts) How many learnable layers does the neural network have? Count only layers that contain trainable parameters. [a] (b) (2 pts) How many parameters does the neural network have? You may disregard bias/offset terms. [b]

Overfitting. When training a neural network, which of the following techniques does NOT help reduce overfitting? 

Summer25_MAT239_FinalExam.pdf

What is the output? matrix = [[0, 1], [2, 3], [4, 5]]  for row in matrix:      print(row[1], end=’ ‘)

What is the output? lst = [10, 20, 30]  lst.insert(1, 15)  print(lst)

What is the output? for i in range(5):      if i == 4:          break      print(i)

Find the output x = 7  x = x % 3  print(x)

What is the output? a = 10  if a < 5:      print("Low")  elif a == 10:      print("Equal")  else:      print("High")

What is the output? lst = [21, 22, 23]  print(lst[0])

What is the output of the following code? print(3 + 4 * 2)