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There's already a decent discussion on how to select the right number of hidden layers and hidden nodes in a feed-forward neural network: How to choose the number of hidden layers and nodes in a feedforward neural network?. However, I struggled to find a detailed discussion on how many hidden layer nodes LSTMs, GRUs or vanilla RNNs need to perform well. We do have some discussion online on other platforms, for example: https://ai.stackexchange.com/questions/3156/how-to-select-number-of-hidden-layers-and-number-of-memory-cells-in-lstm, but answers, on average, seem to be at least about a year old, and given the speed of deep learning research, that may mean they're outdated.

The task I'm currently working on is predicting a single binary outcome from a fixed-length multivariate time series, so we may as well fixate on this specific context of using RNNs.

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