Recurrent Networks and Sequences
Some data comes as a sequence — words in a sentence, notes in a song, the daily price of bread at the Osh bazaar. The order carries meaning. Ordinary neural networks are blind to order. This lesson shows the clever fix: a network that reads one step at a time and carries a little memory forward — the Recurrent Neural Network (RNN).
What you will be able to do after this lesson
- Explain why order matters in sequence data, and why a plain feedforward network cannot see it.
- Describe the core RNN idea: process one element at a time while carrying a hidden state (a running summary), reusing the same weights every step.
- Read an unrolled RNN diagram and follow the hidden state as it flows left to right.
- Trace a tiny RNN by hand across three steps with real numbers.
- Explain the long-memory problem (vanishing gradients) and why early context fades.
- Describe LSTM and GRU gates in plain intuition, plus bidirectional and stacked RNNs.
- State the bottleneck that RNNs never solved — and why that led to the transformer.