Manning Publications | Transfer Learning For Natural Language Processing (2021 EN)

Author: Paul Azunre
Full Title: Transfer Learning For Natural Language Processing
Publisher: Manning Publications (August 31, 2021)
Year: 2021
ISBN-13: 9781617297267 (978-1-61729-726-7)
ISBN-10: 1617297267
Pages: 272
Language: English
Genre: Educational: Data Science
File type: PDF (True), Code Files
Quality: 10/10
Price: $39.99


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.

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

About the reader:
For machine learning engineers and data scientists with some experience in NLP.

-------------
Our members see more. Join us!