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Why does forecasting with an LSTM yield better results with shuffling?

Since you are training a neural network, the optimisation algorithm being used is gradient descent. Gradient descent works by taking small steps towards a solution, one minibatch at a time, which is ...
MuhammedYunus's user avatar
1 vote

How good are LSTMs in generalizing when learning curves?

LSTMs in an encoder-decoder arrangement would ingest $f$, render an encoding, and then decode that into a new sequence $g$. LSTMs are prone to overfitting, especially for small datasets, so I don't ...
MuhammedYunus's user avatar

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