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 explores 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 founder of R7 Speech Science, a San Francisco-based company focused on building innovative products on spoken conversations. Previously, Delip was the founder of Joostware, which specialized in consulting and building IP in natural language processing and deep learning. Delip is a well-cited researcher in natural language processing and machine learning and has worked at Google Research, Twitter, and Amazon (Echo) on various NLP problems. He is interested in building cost-effective, state-of-the-art AI solutions that scale well. Delip has an upcoming book on NLP and deep learning from O’Reilly.
Brian McMahan is a research engineer at Joostware, a San Francisco-based company specialized in consulting and building intellectual property in natural language processing and deep learning. He is also a cofounder at R7 Speech Sciences, a company focused on understanding spoken conversations. 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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