Deep learning: RNNs and LSTM

Recurrent neural networks (RNNs) are a class of neural networks that are naturally suited to processing time-series data and other sequential data. Here we introduce recurrent neural networks as an extension to feedforward networks, in order to allow the processing of variable-length (or even infinite-length) sequences, and some of the most popular recurrent architectures in use, including long short-term memory (LSTM) and gated recurrent units (GRUs). In addition, various aspects surrounding RNNs are discussed in detail, including various probabilistic models that are often realized using RNNs and various applications of RNNs that have appeared within the MICCAI community.