Transfer Learning for Natural Language Processing

English | 2021 | ISBN: 978-1617297267 | 250 Pages | PDF, EPUB, MOBI | 18 MB

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.