Feedforward neural networks are based on input data that is powered to the network and converted into output. If it is a supervised learning algorithm, the output is a label that can recognize the input. Basically, these algorithms connect raw data to specific categories by recognizing patterns. Recurrent networks, on the other hand, take as input not only the current input data that is powered to the network, but also what they have experienced over time.
An recurrent neural network (RNN) is a neural model in which a bidirectional flow of information is present. In other words, while the propagation of signals in feedforward networks takes place only in a continuous manner in a direction from inputs to outputs, recurrent networks are different. In them, this propagation can also occur from a neural layer following a previous one, or between neurons belonging...
