Multi-layer perceptron. Consider the following neural networ…

Multi-layer perceptron. Consider the following neural network defined in PyTorch.  import torch.nn as nnclass 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. (b) (2 pts) How many parameters does the neural network have? You may disregard bias/offset terms.

[FinB] Benji is the behaver. Whenever Heidi uses the can ope…

Benji is the behaver. Whenever Heidi uses the can opener in the kitchen, her dog Benji comes into the kitchen and sits behind her. Benji does this because, in the past, when opened cans in the kitchen, sometimes Chanise opened a can of dog food and gave it to Benji. What is the function-altering effect of the consequence? Heidi gives Benji a can of dog food.