Markov Chain

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

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.

Boltzmann Machine

Boltzmann Machine has a higher capacity than Hopfield Networks BMs are networks that make stochastic, or random order, decisions about whether to be on or off (binary). The capacity corresponds to the number of patterns per neuron that can be recalled.

 

Restricted Boltzmann Machine

To be a valid RBM, all the neurons each layer of the network must not be connected otherwise it’s a regular BM. The network must be trained first in an unsupervised fashion.Using reconstruction (outputs are fed as inputs), RBM uses binary states with a probabilistic model to observe the physical likelihood of sequential events and relationships. RBM is used for detecting patterns in data and finding underlying factors.

 

Deep Belied Network

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.