Applied Natural Language Processing in the Enterprise

Ajay Arasanipalai, Ankur Patel

ISBN 10: 149206257X / ISBN 13: 9781492062578

Published by O'Reilly Media, US, 2021

About this Item

NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP.With a basic understanding of machine learning and some Python experience, youâll learn how to train and deploy real-world NLP applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP.Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehensionTrain NLP models with performance comparable or superior to that of out-of-the-box systemsLearn about transformer architecture and modern tricks like transfer learning that have taken the NLP world by stormBecome familiar with the tools of the trade, including spaCy, Hugging Face, and fast.aiUse Python and PyTorch to build core parts of the NLP pipeline from scratch, including tokenizers, embeddings, and language modelsTake your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production.

Seller Inventory # LU-9781492062578

Bibliographic Details

• Title: Applied Natural Language Processing in the Enterprise
• Publisher: O'Reilly Media, US
• Publication Date: 2021
• Language: English
• ISBN 10: 149206257X
• ISBN 13: 9781492062578
• Binding: Paperback
• Condition: New

About this title

Synopsis

NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP.

With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP.

• Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension
• Train NLP models with performance comparable or superior to that of out-of-the-box systems
• Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm
• Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai
• Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch
• Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production

About the Author

Ankur A. Patel is the Co-Founder and Head of Data at Glean and the Co-Founder of Mellow. Glean uses NLP to extract data from invoices and generate vendor spend intelligence for clients. Mellow is on a mission to democratize NLP tasks such as entity resolution, named entity recognition, and text classification for everyone. Previously, Ankur led teams at 7Park Data, ThetaRay, and R-Squared Macro and began his career at Bridgewater Associates and J.P. Morgan. He is a graduate of Princeton University and lives in New York City. Ajay Arasanipalai is a deep learning researcher and student at University of Illinois at Urbana-Champaign. He's authored many popular articles that discuss state-of-the-art deep learning research. In March 2018, Ajay was invited to speak about accelerated deep learning at Think 2018, IBM's largest annual tech conference. Currently, as cochair of the ACM SIGAI chapter at the University of Illinois, he organizes educational workshops and projects for undergraduate students