Recurrent Neural Networks were created to solve the sequential input data time-series problem. RNN's input is made up of the current input and prior samples. As a result, the node connections create a directed graph. Furthermore, each neuron in an RNN has an internal memory that stores the information from previous samples' computations. Because of their superiority in processing data with a variable input length, RNN models are commonly employed in natural language processing (NLP). The goal of AI in this case is to create a system that can understand human-spoken natural languages, such as natural language modeling, word embedding, and machine translation.
Each successive layer in an RNN is made up of nonlinear functions of weighted sums of outputs and the preceding state. As a result, the basic unit of RNN is termed "cell," and each cell is made up of layers and a succession of cells that allow recurrent neural network models to be processed sequentially.
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