What are Recurrent Neural Networks(RNNs) ?
Recurrent Neural Network is a generalization of feedforward neural network that has an internal memory. RNN is recurrent in nature as it performs the same function for every input of data while the output of the current input depends on the past one computation. After producing the output, it is copied and sent back into the recurrent network. For making a decision, it considers the current input and the output that it has learned from the previous input.
ref:https://towardsdatascience.com/understanding-rnn-and-lstm-f7cdf6dfc14e
Recurrent Neural Networks is a class of artificial neural networks. Artificial Neural Networks replicate the working of human neural networks so that machines can make decisions like a human brain.
Now coming to Recurrent Neural Network.
RNNs are created by programming computers to behave like the human brain.
It is a type of neural network where a series of repeated operations are done over and over on a set of data.
RNNs are then used to identify sequences in data, these patterns are then interpreted to predict upcoming likely outcomes.
In most neural networks, inputs and outputs are independent of each other but in the case of RNNs, the output from the previous step is fed to the machine to predict output of current step.