Markov Chains are distinct from artificial neural networks. Markov processes describe a certain step of events using its statistical nature. The state is memoryless, so the present state depends from the previous state. Common uses for Markov Chains are predicting future weathers given today’s weather and Google’s algorithm in finding out the order of search result.
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.
See Sixte De Maupeou’s website
Deep Belief Networks are stacks of RBM. The last output layer must use SoftMax activation function to create a classifier. Each layer (except the first & last one) are hidden and input at the same time (as on RBM). This network is used for Image/ Face Recognition or Video Sequence recognition.