Q$-1$ Consider a simple feedforward neural network with $3$ input neuron, $3$ hidden neuron, and oneoutput neuron. The network uses the sigmoid activation function for both the hidden and outputlayers. Given an input of $0\mathrm{.}5$ for each neuron and a target output of $0\mathrm{.}8$ and $0\mathrm{.}4$respectively, performone iteration of backpropagation to update the weights using the gradient descent algorithm.Assume the initial weights as $0\mathrm{.}2,$ and the learning rate is $0\mathrm{.}5.$