Transformers
Transformers revolutionized NLP and are now used across AI. They process entire sequences in parallel using self-attention, unlike RNNs which process sequentially.
Key Innovation: "Attention is All You Need" (2017) - Transformers replaced recurrence with attention mechanisms.
Architecture Components
Encoder
Processes input sequence and creates contextual representations using self-attention and feedforward layers.
Decoder
Generates output sequence using encoder outputs, self-attention, and cross-attention mechanisms.
Positional Encoding
Since Transformers don't have inherent sequence order, positional encodings are added to give the model information about token positions.
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Output:
Click "Run Code" to see output
Why Transformers Dominate
- Parallelization: Process entire sequences at once (faster training)
- Long-range Dependencies: Attention can connect any two positions
- Scalability: Performance improves with more data and parameters
- Versatility: Used in GPT, BERT, Vision Transformers, and more