# Complete the entire sequence chapter

Subject: Deep Learning

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/#tee/read/9/tee-c9-s0

Canonical: https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c9-s0/

All sections in this chapter belong to the TEE preparation route. Learn vanilla RNNs first, then the shared-gradient calculation, then LSTM and GRU. Do not skip the gates or BPTT because the MT2 route ends earlier.

![Begin with the vanilla state, then learn why the gated variants carry and update memory differently.](https://deep-learning-study-room.vercel.app/assets/figures/v_rnn_comparison.svg)

Diagram: Begin with the vanilla state, then learn why the gated variants carry and update memory differently.. Labels: What each recurrent cell carries forward | What each recurrent cell carries forward | Compare three different cell designs. These are alternatives, not consecutive | layers. | Vanilla RNN | One hidden state h | No memory gates | LSTM | Cell C and hidden h | Forget / input / | output gates | GRU | One hidden state h | Reset / update gates | At equal dimensions, a standard LSTM uses four affine transforms; a basic GRU | uses three.

Begin with the vanilla state, then learn why the gated variants carry and update memory differently. Tap the diagram to enlarge.