Explaining RNNs without neural networks

This article explains how recurrent neural networks (RNN’s) work without using the neural network metaphor. It uses a visually-focused data-transformation perspective to show how RNNs encode variable-length input vectors as fixed-length embeddings. Included are PyTorch implementation notebooks that use just linear algebra and the autograd feature.