The Basics of Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are widely used for data with some kind of sequential structure. For instance, time series data has an intrinsic ordering based on time. Sentences are also sequential, "I love dogs" has a different meaning than "Dogs I love." Simply put, if the semantics of your data is altered by random permutation, you have a sequential dataset and RNNs may be used for your problem! RNNs are different than the classical multi-layer perceptron (MLP) networks because of two main reasons: 1) They take into account what happened previously and 2) they share parameters/weights.