Derive a gradient descent training rule for a single unit neuron with output o, defined as:

o=w₀ +w₁(x₁ +x_1²)++w_n(x_n +x_n²)

where x₁, x₂, . . . , x_n are the inputs, w₁, w₂, . . . , w_n are the corresponding weights, and w0 is the bias weight. You can assume an identity activation function i.e. f(x) = x. Show all steps of your derivation and the final result for weight update. You can assume a learning rate of .