Transfer Learning for Natural Language Processing
- 4h 26m
- Paul Azunre
- Manning Publications
- 2021
Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems.
Summary
In Transfer Learning for Natural Language Processing you will learn:
- Fine tuning pretrained models with new domain data
- Picking the right model to reduce resource usage
- Transfer learning for neural network architectures
- Generating text with generative pretrained transformers
- Cross-lingual transfer learning with BERT
- Foundations for exploring NLP academic literature
Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation.
About the book
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 practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications.
What's inside
- Fine tuning pretrained models with new domain data
- Picking the right model to reduce resource use
- Transfer learning for neural network architectures
- Generating text with pretrained transformers
In this Book
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About This Book
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About the Cover Illustration
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What is Transfer Learning?
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Getting Started with Baselines—Data Preprocessing
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Getting Started with Baselines—Benchmarking and Optimization
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Shallow Transfer Learning for NLP
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Preprocessing Data for Recurrent Neural Network Deep Transfer Learning Experiments
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Deep Transfer Learning for NLP with Recurrent Neural Networks
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Deep Transfer Learning for NLP with the Transformer and GPT
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Deep Transfer Learning for NLP with BERT and Multilingual BERT
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ULMFiT and Knowledge Distillation Adaptation Strategies
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ALBERT, Adapters, and Multitask Adaptation Strategies
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Conclusions
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