In simple words: LSTM is an RNN with gates that manage information over time.
A better memory design
Long Short-Term Memory (LSTM) is a kind of RNN. Its gates control what information to keep, add, or forget as the sequence continues. This helps with longer dependencies, though it does not create perfect memory.
Where it is useful
LSTMs have been used for sentences, speech, customer activity over time, and other ordered data. Modern Transformers often handle long text better, but LSTMs remain useful to understand and can suit some sequence problems.
See the flow
sequence→LSTM gates→updated memory
Simple example
In “I ordered a laptop last week. It arrived damaged,” the model should keep “laptop” in mind when reading “It”.
Remember: LSTM is an RNN with gates that manage information over time.