Natural language processing (NLP) involves the application of machine learning and other statistical techniques to derive insights from human language. With large volumes of data exchanged as text (in the form of documents, tweets, email, chat, and so on), NLP techniques are indispensable to modern intelligent applications. The applications range from enterprise to pedestrian.
Delip Rao and Brian McMahan explore natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.
Environment setup and data download
Representations for words: Word embeddings
Deep learning frameworks
Feed-forward networks for NLP
Modeling subword units with convolutional networks
Recurrent neural networks (RNNs) to model sequences
Structured prediction methods
From sequence models to sequence-to-sequence models
DL modeling for common NLP tasks
Choose your own adventure
DL for NLP: Best practices
Wrap-up and Q&A
Delip Rao is the vice president of research at the AI Foundation, where he leads speech, language, and vision research efforts for generating and detecting artificial content. Previously, he founded the AI research consulting company Joostware and the Fake News Challenge, an initiative to bring AI researchers across the world together to work on fact checking-related problems, and he was at Google and Twitter. Delip is the author of a recent book on deep learning and natural language processing. His attitude toward production NLP research is shaped by the time he spent at Joostware working for enterprise clients, as the first machine learning researcher on the Twitter antispam team, and as an early researcher at Amazon Alexa.
Brian McMahan is a data scientist at Wells Fargo, working on projects that apply natural language processing (NLP) to solve real world needs. Recently, he published a book with Delip Rao on PyTorch and NLP. Previously, he was a research engineer at Joostware, a San Francisco-based company specializing in consulting and building intellectual property in NLP and Deep Learning. Brian is wrapping up his PhD in computer science from Rutgers University, where his research focuses on Bayesian and deep learning models for grounding perceptual language in the visual domain. Brian has also conducted research in reinforcement learning and various aspects of dialogue systems.
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