by Michele Laurelli
A neural network architecture designed for sequential data, with connections that loop back to previous states.
RNNs process sequences by maintaining a hidden state that captures information from previous time steps. They excel at tasks with temporal dependencies but suffer from vanishing gradients. LSTM and GRU address this issue.
Time series forecasting
Text generation
Speech recognition