Delip Rao is the founder of Joostware, a San Francisco based consulting company specializing in machine learning and natural language processing research. At Joostware, he has worked closely with customers from Fortune 500 and other companies to help leaders understand what it means to bring AI to their organization, and translate their product/business vision to an AI implementation roadmap. He also provides technology due-diligence services to VC firms in the Valley.

He is also cofounder of the Fake News Challenge, an initiative to bring hackers and AI researchers to work on fact-checking related problems in news. Delip previously worked on NLP research and products at Twitter and Amazon (Alexa). He blogs on NLP and deep learning at deliprao.com

Brian McMahan is a research engineer at Wells Fargo focusing on NLP. Previously, he worked on NLP research at Joostware, a San Francisco-based consulting company specializing in machine learning and natural language processing research. He has a PhD in Computer Science from Rutgers University where he built Bayesian and Deep Learning models of language and semantics as they apply to machine perception in interactive situations.

Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.

Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations.

Explore computational graphs and the supervised learning paradigm

Master the basics of the PyTorch optimized tensor manipulation library

Get an overview of traditional NLP concepts and methods

Learn the basic ideas involved in building neural networks

Use embeddings to represent words, sentences, documents, and other features

Explore sequence prediction and generate sequence-to-sequence models

Learn design patterns for building production NLP systems