Transfer Learning for Natural Language Processing

English | 2021 | ISBN: 1617297267 | 266 pages | True PDF | 6.78 MB

Transfer Learning for Natural Language Processing gets you up to speed with the relevant ML concepts before diving into the cutting-edge advances that are defining the future of NLP.

Building and training deep learning models from scratch is costly, time-consuming, and requires massive amounts of data. To address this concern, cutting-edge transfer learning techniques enable you to start with pretrained models you can tweak to meet your exact needs. In Transfer Learning for Natural Language Processing, you’ll go hands-on with customizing these open source resources for your own NLP architectures.

Transfer Learning for Natural Language Processing gets you up to speed with the relevant ML concepts before diving into the cutting-edge advances that are defining the future of NLP. You’ll learn how to adapt existing state-of-the art models into real-world applications, including building a spam email classifier, a movie review sentiment analyzer, an automated fact checker, a question-answering system and a translation system for low-resource languages.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.