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
Ankur A. Patel is an AI entrepreneur, thought leader, and author. He is currently the cofounder and head of data at Glean and the cofounder of Mellow. Glean uses natural language processing to deliver vendor spend intelligence within an accounts payable solution. Mellow develops easy-to-use natural language processing APIs for developers to use as part of their product build.
Previously, Ankur was the vice president of data science at 7Park Data, a Vista Equity Partners portfolio company. Ankur used alternative data to build alternative data products for hedge funds and developed a natural language processing-based entity recognition, resolution, and linking platform for enterprise clients. Prior to 7Park Data, Ankur led data science efforts in New York City for Israeli artificial intelligence firm ThetaRay, a pioneer in applied unsupervised learning.
Ankur began his career as an analyst at JPMorgan and then became the lead emerging markets sovereign credit trader for Bridgewater Associates, the world's largest global macro hedge fund. He later founded and managed R-Squared Macro, a machine learning-based hedge fund.
A graduate of the Woodrow Wilson School at Princeton University, Ankur is the recipient of the Lieutenant John A. Larkin Memorial Prize. He currently resides 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.
