Hopfield Network is an example of markov chain, each node acts as its own input, hidden and output neuron. Before training, a desired outcome has an unique neuron state vector. When training, the weights are calibrated through activation thresholds. When weights are established and unchanging, the network is fully trained. Afterwards, the test neuron state vector will follow the allocated weights to minimize “energy”/ temperature so that the states become the desired ones. HN is an alternative way to classify images without using gradients and backpropagation. It is used for reconstruction of images.