Sequence Models
Sequence-to-Sequence — Translating Input Sequences to Output Sequences
Seq2seq models are the backbone of machine translation, summarization, and dialogue systems. They encode variable-length inputs into a fixed representation and decode it into variable-length outputs, enabling end-to-end learning of sequence transduction.
- Key point 1 — Encoder-decoder architecture maps variable-length input to variable-length output
- Key point 2 — Teacher forcing stabilizes training but introduces exposure bias
- Key point 3 — Beam search and attention mechanisms unlock high-quality generation
"To translate is to understand — and seq2seq models learn to understand language itself."
Sequence-to-Sequence Models
Seq2seq models map an input sequence to an output sequence of potentially different length. They are the foundation of machine translation, summarization, and dialogue systems.
Encoder-Decoder Architecture
Context Vector
Teacher Forcing
Decoding Strategies
Greedy Decoding
Beam Search
Complete PyTorch Implementation
Training
Practice Exercises
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Implement beam search: Write a beam search decoder with length normalization. Compare outputs with greedy decoding.
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Scheduled sampling: Modify the training loop to use scheduled sampling with linear decay of from 1.0 to 0.0.
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Transformer decoder: Replace the RNN decoder with a Transformer decoder. Compare performance and training speed.
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Summarization task: Train a seq2seq model on CNN/DailyMail dataset for text summarization. Evaluate with ROUGE scores.
Key Takeaways
What to Learn Next
-> Attention Mechanisms Discover how attention solves the information bottleneck in sequence models.
-> LSTM Networks Explore gated recurrent units with cell state for long-range dependencies.
-> GRU Networks Learn a simpler gating mechanism with fewer parameters and comparable performance.
-> RNN Deep Dive Understand recurrent fundamentals and the vanishing gradient problem.
-> Vision Transformers Apply Transformer architecture to image recognition by treating patches as tokens.
-> DL Systems Design Master distributed training, monitoring, and production deployment of deep learning models.