The goal of an RNN is consistent data usage under the assumption that there is some dependency between consecutive data elements. In traditional neural networks, it is understood that all inputs and outputs are independent. But for many tasks, this independence is not suitable. If you want to predict the next word in a sentence, for example, knowing the sequence of words preceding it is the most reliable way to do so. RNNs are recurrent because they perform the same task for each element of the sequence, and the output is dependent on previous calculations.
In other words, RNNs are networks that have feedback loops and memory. RNNs use memory to take into account prior information and calculations results. The idea of a recurrent network can be represented as follows:
In the preceding diagram, a fragment of the neural network, 
(a layer of neurons...
