CheatSheet | Long Short-Term Memory (LSTM)

AIM Media HouseContributor

Published 2020-11-23T05:30:00.000+05:30Updated 2024-12-31T05:30:00.000+05:30

Key takeaways

LSTM addresses the vanishing gradient problem found in traditional recurrent neural networks.
This architecture enables the retention of essential information over longer sequences of data.
LSTM operates similarly to RNN but differs in its handling of cell states.

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Recurrent neural networks possess a problem of vanishing gradients and eventually running out of memory to save essential information. To overcome this a modified version of RNN called LSTM or long short term memory was introduced. It is similar in operation to RNN but the difference lies in the cell states.

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