The output of a layer is stored and sent back to the input in this network. As a result, the nodes of a specific layer retain some information about previous actions. The total of weights and features determines the input layer's combination. The hidden layers are where the rnn - based process begins. Each node here remembers part of the information from the previous stage. The model saves some data from each iteration so that it can be used later. When the system's outcome is incorrect, it self-learns. It then uses that knowledge to optimize the accuracy of its back-propagation forecast. Text-to-speech technology is the most common application of RNN.