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CMU Advanced NLP: Recurrent Neural Networks

Graham Neubig via YouTube

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Overview

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This advanced lecture examines recurrent neural networks for natural language processing, covering prediction types, long-distance dependencies, vanishing gradients, LSTMs, sentence modeling, efficiency, optimization, and pre-training.

Syllabus

Intro
Long Distance Dependencies
Winigrad Schema Challenge
Types of Prediction
Unconditioned vs Condition Prediction
Types of Unconditioned Prediction
Types of Condition Prediction
Recurrent Neural Networks
Vanishing Gradients
LTSM
RNNs
Other examples
Efficiency
Optimization

Taught by

Graham Neubig

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