Transfer Learning for Natural Language Processing, Paul Azunre

Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems.

Summary

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.

About the author

Paul Azunre

holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs.

Table of Contents

PART 1 INTRODUCTION AND OVERVIEW

1 What is transfer learning?

2 Getting started with baselines: Data preprocessing

3 Getting started with baselines: Benchmarking and optimization

PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS)

4 Shallow transfer learning for NLP

5 Preprocessing data for recurrent neural network deep transfer learning experiments

6 Deep transfer learning for NLP with recurrent neural networks

PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES

7 Deep transfer learning for NLP with the transformer and GPT

8 Deep transfer learning for NLP with BERT and multilingual BERT

9 ULMFiT and knowledge distillation adaptation strategies

10 ALBERT, adapters, and multitask adaptation strategies

11 Conclusions

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