Applied Natural Language Processing in the Enterprise

NLP is one of the hottest topics in AI today.

Having lagged for years behind other deep learning fields such as computer vision, NLP only recently gained mainstream popularity. Google, Facebook, and OpenAI have open-sourced large pretrained language models, but many organizations today still struggle with building and adopting NLP applications. This hands-on guide helps you learn the process quickly.

If you have a basic to intermediate understanding of machine learning and programming experience with Python, you'll learn how to build and deploy real-world NLP applications in your organization.

Authors Ankur Patel and Ajay Uppili Arasanipalai walk you through the process without bogging you down in theory.

• Understand how state-of-the-art NLP models work
• Learn the tools of the trade, including frameworks popular today
• Perform NLP tasks such as text classification, semantic search, and reading comprehension
• Solve problems using new models like transformers and techniques such as transfer learning
• Build NLP models from scratch with performance comparable or superior to out-of-the-box systems
• Deploy your models to production and maintain their performance
• Implement a suite of NLP algorithms using Python and PyTorch

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

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