Transfer Learning for Natural Processing

Transfer learning for natural language processing (NLP) is a technique that allows you to start with pretrained models and tweak them to meet your specific needs. This saves time and computational costs, and can deliver state-of-the-art results even with limited label data. Paul Azunre's book "Transfer Learning for Natural Language Processing" provides a practical primer to transfer learning techniques and shows how to adapt existing state-of-the-art models into real-world applications.

Format: Paperback / softback
Length: 272 pages
Publication date: 27 October 2021
Publisher: Manning Publications

Building and training deep learning models from scratch is a costly, time-consuming, and data-intensive process. To address this concern, cutting-edge transfer learning techniques enable you to start with pretrained models that you can customize to meet your specific needs. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre takes you hands-on with customizing these open-source resources for your own NLP architectures. You'll learn how to use transfer learning to deliver state-of-the-art results even when working with limited label data, all while saving on training time and computational costs.

About the technology:
Transfer learning enables machine learning models to be initialized with existing prior knowledge. Initially pioneered in computer vision, transfer learning techniques have been revolutionizing Natural Language Processing with significant reductions in the training time and computation power needed for a model to start delivering results. Emerging pretrained language models such as ELMo and BERT have opened up new possibilities for NLP developers working in machine translation, semantic analysis, business analytics, and natural language generation.

About the book:
Transfer Learning for Natural Language Processing is a practical primer to transfer learning techniques capable of delivering huge improvements to your NLP models. Written by DARPA researcher Paul Azunre, this practical book 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, and a question-answer system.

ISBN-13: 9781617297267