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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
About the author
Ankur A. Patel is the Vice President of Data Science at 7Park Data, a Vista Equity Partners portfolio company. At 7Park Data, Ankur and his data science team use alternative data to build data products for hedge funds and corporations and develop machine learning as a service (MLaaS) for enterprise clients. MLaaS includes natural language processing (NLP), anomaly detection, clustering, classification, and time series prediction. Prior to 7Park Data, Ankur led data science efforts in New York City for Israeli artificial intelligence firm ThetaRay, one of the world's pioneers in applied unsupervised learning.
Ajay Uppili Arasanipalai is an undergraduate at the University of Illinois at Urbana-Champaign and a deep learning researcher. Ajay has worked for a number of top AI companies including FloydHub, Weights & Biases, and Nanonets and has written numerous popular articles on how state-of-the-art deep learning models work. In March 2018, Ajay was invited to speak about accelerated deep learning at Think 2018, IBM's largest annual tech conference. He is currently working on independent research projects related to data augmentation for language models and visualizations for transformer networks.
Summary
This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With some Python experience and a basic understanding of machine learning, you'll learn how to build and deploy real-world NLP applications in your organization.
