Recurrent, Long Short-Term Memory and Transformer Networks

Recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformer networks all are used for processing sequential data, but they differ in their architectures and capabilities. RNNs are the foundational model, LSTMs are an improved type of RNN designed to handle long-range dependencies, and transformers have an architecture that uses attention mechanisms for parallel processing and capturing long-range dependencies. Essential features of an RNN are mentioned. The nomenclature of RNNs is introduced, and the general steps in an RNN algorithm are explained. The components of an LSTM architecture—specifically, the memory cell and the roles of its gates—are discussed. The gate-wise responsibilities in the information control process are clarified. The cell, hidden, and output states of LSTM are defined, followed by a brief description of LSTM setup and training. The components of a transformer neural network and its operational mechanism are studied.