Introduction of Recurrent Neural Networks (RNN) - Ankitaism

The Artificial Neural Networks (ANN) have evolved tremendously with a variety of networks to suit for applications according their individual properties. The ANN have a simple structure consisting of nodes (also called processing units) connected to each other via weights. The network gets stimulated by giving input to few are all nodes, and this stimulation, also called activation spreads through entire network. The way in which layers are connected and fed categorizes ANNs in to feed-forward networks (FFN) or feed-back networks (FBN). The FFNs are acyclic in nature i.e. just one forward travelling of weights and biases; whereas the FBNs are cyclically connected i.e. some layers have a connection coming from the other layers recursively.