Introduction: Recurrent Neural Networks
Explore the fundamentals of recurrent neural networks and their role in processing sequential data like text and time series. Understand parameter sharing, state variables, and how RNNs predict sequence values. Learn RNN applications including one-to-one and many-to-many models, and see practical use in named entity recognition using character and token embeddings.
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Overview of recurrent neural networks
Recurrent neural networks (RNNs) are a special family of neural networks that are designed to cope with sequential data (that is, time-series data), such as stock market prices or a sequence of texts (for example, variable-length sentences). RNNs maintain a state variable that captures the various patterns present in sequential data; therefore, they are able to model sequential data. In comparison, conventional feed-forward neural networks don’t have this ability unless the data is represented with a feature representation that ...