Q$\mathrm{.}3)$ Explain how a Hopfield Network $($HN$)$ can be made to learn the noise$-$free representation of a given

pattern, like say a B&W image. Show how hebbian learning maps this noise$-$free representation into

a stationary state of the considered $HN,$ and that this stationsry state indeed coriesponds to the

lowest energy e$.$ most stable$)$ state of the HN$.$