A standard RNN (Recurrent Neural Network) and an LSTM (Long Short-Term Memory) are both types of recurrent neural networks, but they differ in their architecture and functionality.

  The main difference between a standard RNN and an LSTM lies in their ability to handle long-term dependencies in sequential data. Standard RNNs suffer from the "vanishing gradient" problem, where the gradients become exponentially small as they propagate back in time, making it difficult for the network to learn and capture long-term dependencies. LSTM was proposed to overcome this limitation.

  LSTM introduces a memory cell as a fundamental building block, which allows the network to remember information over long sequences. The memory cell is equipped with three gates: the input gate, the forget gate, and the output gate. These gates regulate the flow of information into and out of the memory cell, enabling the LSTM to selectively retain or forget information based on the context. This gating mechanism allows LSTMs to overcome the vanishing gradient problem and capture long-term dependencies more effectively.

  In comparison, standard RNNs do not have explicit mechanisms to remember or forget information over time. They process each input sequentially and update their hidden state at each step based on the current input and the previous hidden state. However, their ability to capture long-term dependencies is limited due to the vanishing gradient problem.

  In summary, while both standard RNNs and LSTMs are recurrent neural networks designed for sequential data, LSTMs have an additional memory cell and gating mechanism, which enable them to capture and retain long-term dependencies better than standard RNNs.