Synopsis
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.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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
"synopsis" may belong to another edition of this title.
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. He founded Algorine Inc., a Research Lab dedicated to advancing AI/ML and identifying scenarios where they can have a significant social impact. Paul also co-founded Ghana NLP, an open source initiative focused using NLP and Transfer Learning with Ghanaian and other low-resource languages. He frequently contributes to major peer-reviewed international research journals and serves as a program committee member at top conferences in the field.
"About this title" may belong to another edition of this title.
Search results for Transfer Learning for Natural Language Processing
Stock Image
Transfer Learning for Natural Language Processing
Seller: BooksRun, Philadelphia, PA, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: Fair. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way. Seller Inventory # 1617297267-7-1
Stock Image
Transfer Learning for Natural Language Processing
Seller: HPB-Red, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Seller Inventory # S_426068704
Stock Image
Transfer Learning for Natural Language Processing
Seller: Bookmans, Tucson, AZ, U.S.A.
Seller rating 5 out of 5 stars
paperback. Condition: Good. Satisfaction 100% guaranteed. Seller Inventory # mon0002678896
Stock Image
Transfer Learning for Natural Processing
Seller: WorldofBooks, Goring-By-Sea, WS, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Seller Inventory # GOR013786078
Buy Used
US$ 4.48
US$ 7.55 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available
Stock Image
Transfer Learning for Natural Language Processing
Seller: medimops, Berlin, Germany
Seller rating 5 out of 5 stars
Condition: as new. Wie neu/Like new. Seller Inventory # M01617297267-N
Buy Used
US$ 26.45
US$ 11.64 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 1 available
Stock Image
Transfer Learning for Natural Language Processing
Seller: GoldBooks, Denver, CO, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. New Copy. Customer Service Guaranteed. Seller Inventory # 44S31_72_1617297267
Stock Image
Transfer Learning for Natural Language Processing Format: Paperback
Seller: INDOO, Avenel, NJ, U.S.A.
Seller rating 5 out of 5 stars
Condition: As New. Unread copy in mint condition. Seller Inventory # SS9781617297267
Stock Image
Transfer Learning for Natural Language Processing Format: Paperback
Seller: INDOO, Avenel, NJ, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Brand New. Seller Inventory # 9781617297267
Seller Image
Transfer Learning for Natural Language Processing
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 42626022-n
Seller Image
Transfer Learning for Natural Processing
Published by
Manning Publications, US, 2021
ISBN 10: 1617297267
ISBN 13: 9781617297267
New
Paperback
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: New. Building and training deep learning models from scratch is costly, time-consuming, and requires massive amounts of data. To address this concern, cutting-edge transfer learning techniques enable you to start with pretrained models you can tweak to meet your exact 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 technologyTransfer learning enables machine learning models to be initialized with existing prior knowledge. Initially pioneered in computer vision, transfer learning techniques have been revolutionising Natural Language Processing with big 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, a question-answering system and a translation system for low-resource languages. what's inside Fine tuning pretrained models with new domain dataPicking the right model to reduce resource usageTransfer learning for neural network architecturesFoundations for exploring NLP academic literature about the readerFor 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. He founded Algorine Inc., a Research Lab dedicated to advancing AI/ML and identifying scenarios where they can have a significant social impact. Paul also co-founded Ghana NLP, an open source initiative focused using NLP and Transfer Learning with Ghanaian and other low-resource languages. He frequently contributes to major peer-reviewed international research journals and serves as a program committee member at top conferences in the field. Seller Inventory # LU-9781617297267