Explaining RNNs without neural networks

Explaining RNNs without neural networks

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.

Source: explained.ai/rnn/index.html

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