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Transfer Learning for Natural Language Processing /
Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Shelter Island :
Manning,
[2021]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Part 1 Introduction and overview
- 1 What is transfer learning?
- 2 Getting started with baselines: Data preprocessing
- 3 Getting started with baselines: Benchmarking and optimization
- Part 2 Shallow transfer learning and deep transfer learning with recurrent neural networks (RNNs)
- 4 Shallow transfer learning for NLP
- 5 Preprocessing data for recurrent neural network deep transfer learning experiments
- 6 Deep transfer learning for NLP with recurrent neural networks
- Part 3 Deep transfer learning with transformers and adaptation strategies
- 7 Deep transfer learning for NLP with the transformer and GPT
- 8 Deep transfer learning for NLP with BERT and multilingual BERT
- 9 ULMFiT and knowledge distillation adaptation strategies
- 10 ALBERT, adapters, and multitask adaptation strategies
- 11 Conclusions
- Appendix A Kaggle primer
- Appendix B Introduction to fundamental deep learning tools.