seniorDeep Learning
What is the Transformer Architecture and why did it replace RNNs and CNNs in NLP?
Updated May 16, 2026
Short answer
The Transformer is a deep learning architecture based entirely on self-attention mechanisms that replaces recurrence and convolution for sequence modeling.
Deep explanation
The Transformer architecture revolutionized NLP by removing recurrence and convolution entirely and relying solely on attention mechanisms.
Core architecture: It consists of:
- Encoder (optional in decoder-only models)
- Decoder
Key components:
- Self-Attention:
- Models relationships between tokens.
- Feedforward Networks:
- Apply non-linear transformations.
- Residual Connections:
- Improve gradient flow.
- Layer Normalization:
- Stabilizes training.
Why it replaced RNNs:
- RNNs are sequential and slow.
- Transformers enable parallel computation.…
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