Unrolled RNN in Time
- Input \( x_1,x_2,x_3,\dots \) feed sequentially; the hidden state flows from one step to the next, capturing past context.
- After processing the final input \( x_T \), the network can make a prediction (many-to-one) or outputs can be produced at each step (many-to-many).
- Unrolling clarifies that training an RNN is like training a deep feedforward network of depth T, with recurrent connections tying layers together.