A Long Short-Term Memory (LSTM) network is a type of Recurrent Neural Network (RNN) designed to handle long-range dependencies in sequential data. It overcomes the vanishing gradient problem, which often limits standard RNNs, making LSTMs particularly useful for processing text, speech, and time-series data.

How LSTMs Work

LSTMs introduce a memory cell and three gates that regulate information flow:

• Forget Gate – Decides what past information to discard.

• Input Gate – Determines what new information to store.

• Output Gate – Controls what information to output at each step.

This gated structure enables LSTMs to retain important context over long sequences, making them effective in tasks like speech recognition, machine translation, and stock price prediction.

LSTMs vs. Other Architectures

Compared to basic RNNs, LSTMs are much better at remembering long-term patterns. However, modern AI models, especially Transformers, have largely replaced LSTMs in fields like natural language processing. Despite this, LSTMs remain valuable in real-time and low-power AI applications where sequential processing is crucial.