Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems.SummaryInTransfer Learning for Natural Language Processingyou will learn:Fine tuning pretrained models with new domain dataPicking the right model to reduce resource usageTransfer learning for neural network architecturesGenerating text with generative pretrained transformersCross-lingual transfer learning with BERTFoundations for exploring NLP academic literatureTraining 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.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyBuild 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 bookTransfer Learning for Natural Language Processingteaches 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 insideFine tuning pretrained models with new domain dataPicking the right model to reduce resource useTransfer learning for neural network architecturesGenerating text with pretrained transformersAbout the readerFor machine learning engineers and data scientists with some experience in NLP.About the authorPaul Azunreholds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs.Table of ContentsPART 1 INTRODUCTION AND OVERVIEW1 What is transfer learning?2 Getting started with baselines: Data preprocessing3 Getting started with baselines: Benchmarking and optimizationPART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS)4 Shallow transfer learning for NLP5 Preprocessing data for recurrent neural network deep transfer learning experiments6 Deep transfer learning for NLP with recurrent neural networksPART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES7 Deep transfer learning for NLP with the transformer and GPT8 Deep transfer learning for NLP with BERT and multilingual BERT9 ULMFiT and knowledge distillation adaptation strategies10 ALBERT, adapters, and multitask adaptation strategies11 Conclusions