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
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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