MIT 6.7960 L10: Memory and Sequence Modeling — RNNs, LSTMs, and Vanishing/Exploding Gradients
An RNN compresses the past into a hidden state, but recurrence makes gradients multiply over time — they either vanish or explode; LSTM decouples 'memory' from 'update' via input/forget/output gates so long-range information flows stably. Attention later replaced it because it reaches any history in O(1).