Understanding the foundation of neural machine translation and how encoder-decoder architectures transformed NLP.
The breakthrough that taught neural networks to focus - exploring the first attention mechanism in seq2seq models.
Simplified attention mechanisms that improved efficiency while maintaining translation quality.
How self-attention eliminated recurrence and enabled the Transformer revolution.
The complete architecture that changed everything - from positional encoding to multi-head attention.