10
	Weights  Energies  Probabilities
		• Each possible joint configuration of the visible and hidden units has a Hopfield “energy” –  The energy is determined by the weights and biases. • The energy of a joint configuration of the visible and hidden units determines the probability that the network will choose that configuration. • By manipulating the energies of joint configurations, we can manipulate the probabilities that the model assigns to visible vectors. – This gives a very simple and very effective learning algorithm.