Question $3[3$ pts$].$ Given a neuron with $n$ dimensional input features $[x_1,x_2,...,x_n],$ assuming the output from the neuron is defined by

$o=w_0+w_1{x}_1+w_2{x}_2+w_3{x}_3...+w_n{x}_n$

The neuron uses identify function as the activation function, and an L$-2$ norm regularizer is added to the objective function, with $$ being the regularizer's coefficient.

$$ Please draw the structure of the neuron $($please show input, output, and the weight$)[1$ pt$]$

$$ Given a batch of N training instances $($x$(1),$ d$(1)),($x$(2),$ d$(2)),...,($x$($N$),$ d$($N$)),$ where d$($i$)$ denotes label of instance x$($i$),$ show gradient descent learning objective function of the neuron $[1$ pt$]$

$$ Derive gradient descent learning weight updating rule for weight wi $[1$ pt$].$