Transfer Learning for Natural Language Processing


Transfer Learning for Natural Language Processing
by: Paul Azunre
Publisher Finelybook 出版社:Manning Publications (August 31, 2021)
Language 语言:English
pages 页数:272 pages
ISBN-10 书号:1617297267
ISBN-13 书号:9781617297267

Book Description
Build custom NLP models in record time by: adapting pre-trained machine learning models to solve specialized problems.

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.

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.

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.


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