Ch 10: Natural Language Processing Basics - Advanced¶
Track: Practitioner | Try code in Playground | Back to chapter overview
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To run the code interactively, clone the repo and open chapters/chapter-10-natural-language-processing-basics/notebooks/03_nlp_advanced.ipynb in Jupyter.
Chapter 10: NLP Basics — Notebook 03 (Advanced)¶
This notebook introduces attention mechanisms, sequence-to-sequence concepts, transfer learning in NLP (e.g. BERT), multi-task systems, language generation basics, and production considerations. It sets the stage for Chapter 11: Large Language Models & Transformers.
What you'll learn¶
| Topic | Section |
|---|---|
| Attention in NLP (scaled dot-product, self-attention) | §1 |
| Seq2seq and applications | §2 |
| Transfer learning (BERT/DistilBERT) | §3 |
| Multi-task NLP system | §4 |
| Language generation basics | §5 |
| Production (serialization, inference, monitoring) | §6 |
| Preview of Chapter 11 and capstone design | §7–8 |
Time estimate: 2.5–3 hours
Key concepts¶
- Attention — Let the model focus on different parts of the input when producing output.
- Transfer learning — Fine-tune pre-trained language models (e.g. BERT) on your task.
- Production — Save/load pipelines (e.g. joblib), batch inference, error handling, monitoring.
Run the full notebook for code and outputs.
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