Why RNNs exist

Many problems are sequential:

• text
• time series
• audio

An RNN processes inputs one step at a time and carries a hidden state.

  flowchart LR
  X1[x1] --> H1[h1]
  H1 --> H2[h2]
  X2[x2] --> H2
  H2 --> H3[h3]
  X3[x3] --> H3

What the hidden state means

Hidden state = memory of previous inputs.

Limitations

Classic RNNs struggle with long-range dependencies.

Improved variants:

• LSTM
• GRU

And modern NLP often uses Transformers.

Mini-checkpoint

What kind of data is best suited for RNN-like architectures?

• sequences where order matters.