Recurrent Neural Networks

An RNN applies a recurrent transformation that combines the current input with the previous hidden state. Shared parameters allow the same transition to operate across sequence positions. The state summarizes earlier information for prediction at each step or at the sequence end. Backpropagation through time trains those transitions, and long dependencies can create vanishing or exploding gradients. Gated variants such as LSTM and GRU change how information is retained and updated. Bidirectional recurrence can use both directions when the complete sequence is available, but is incompatible with strict online use if it requires future observations.

Define whether outputs are needed per step or per sequence and choose padding, masking and truncation accordingly. Specify when state resets and whether it is carried between batches. Check time ordering, target alignment and gradient stability, using clipping or gated alternatives where justified. Validate on independent sequences or future periods and compare simpler temporal baselines. The deliverable should include a model and state-management contract, ensuring that training, evaluation and streaming inference interpret sequence continuity and available information in the same way.

Long-range dependencies can be difficult to learn, and hidden-state mistakes can leak information between unrelated sequences. Padding and target alignment errors may execute without exceptions. Recurrence can limit parallelism across time. A hidden state is not a complete or interpretable memory of past events. RNNs differ from state-space and attention-based models despite overlapping sequence tasks. Test length sensitivity, state resets and the prediction-time boundary before drawing conclusions from a good score on conveniently segmented sequences.