LOSS FUNCTION TO BE USED: Binary Cross Entropy Loss Function Please do not post the same solution again or post solution copied from Chegg or other online sources. Please do not respond unless you...

LOSS FUNCTION TO BE USED: Binary Cross Entropy Loss Function

Please do not post the same solution again or post solution copied from Chegg or other online sources. Please do not respond unless you know the solution, it prevents others from providing correct solution

Extracted text: Input 1 W11 Hidden 1 W13 W21 Output (3) W12 W23 Input 2 Hidden 2 W22


Extracted text: 4 Backpropagation Consider the following network with sigmoid activation functions in the hidden and output neurons, and binary cross entropy loss function. Assume we initialize the weights as follows: W11 = 1, W12 = 0.5, W21 = 0.1, W22 = 0.2, W13 = 1, W23 = 0.5. The biases for the hidden nodes are initialized as bl = 0.1, 62 = 0.1, and for the the output node is initialized as 63 = 0.5. Backward pass: Calculate the derivative of the loss w.r.t W11. What's the value of the derivative for the initialized weights, input 1 = 0.1, input 2 = 0.2, and label = 1? How do you update W11 using gradient descent, based on the derivative you derived in the previous part and learning rate 7 = 0.1? What is the value of the loss using the updated W11? How did it change after the update?