One fundamental difference between other neural networks and RNNs is that, in all other networks, the inputs are independent of each other. However, in an RNN, all the inputs are related to each other. In an application, to predict the next word in a given sentence, the relationship between all the previous words helps to predict the current output. In other words, an RNN remembers all these relationships while training itself. This is not the case with other types of neural networks. A representation of a feedforward network is illustrated in the following diagram:
Feedforward neural network ...
