2. The following neural network uses the sigmoid function as the activation function for all nodes. The output
nodes indicates whether or not a person has the disease - C1 for no, C2 for yes.
You pass in a training example that has the disease, and get outputs from all of the nodes as follows:
A1 = 0.2
A2 = 0.8
B1 = 0.7
B2 = 0.5
C1 = 0.4
C2 = 0.6
Backpropagate the error to adjust the indicated weight (w), using a learning rate of 1. What is the new w after
backpropagation?
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