1. Convolutional Neural Networks (CNN) — for text classification

2. Recurrent Neural Networks (RNN)

3. Vanishing / Exploding gradient problem

4. Long Short-Term Memory (LSTM)

5. Gated Recurrent Unit (GRU)

6. Bidirectional RNNs

7. Sequence-to-Sequence (Seq2Seq) models

8. Encoder-Decoder architecture

9. Attention mechanism (Bahdanau, Luong)

10. Self-attention

11. Multi-head attention

12. Positional encoding

13. The Transformer architecture

14. BERT & variants (RoBERTa, ALBERT, DistilBERT)

15. GPT & autoregressive language models

16. T5, BART (seq2seq transformers)

17. Large Language Models (LLMs) — architecture level