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Intro

NLP and Sequential Data

Long-distance Dependencies in Language

Parameter Tying

What Can RNNs Do?

e.g. Language Modeling

Representing Sentences

Representing Contexts

Recurrent Neural Networks in DyNet

Parameter Initialization

Sentence Initialization

A Solution: Long Short-term Memory (Hochreiter and Schmichuber 1997)

Other Alternatives

Handling Mini-batching

Mini-batching Method

Handling Long Sequences

Example: LM - Sentence Classifier

LSTM Structure

Description:

Explore recurrent neural networks in this lecture from CMU's Neural Networks for NLP course. Dive into the fundamentals of recurrent networks, addressing challenges like vanishing gradients through LSTMs. Analyze the strengths and weaknesses of recurrence in sentence modeling and discover pre-training techniques for RNNs. Access accompanying slides and code examples to reinforce your understanding of key concepts including parameter tying, language modeling, sentence representation, and handling long sequences with mini-batching methods.

Neural Nets for NLP 2017 - Recurrent Neural Networks

Graham Neubig

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#Computer Science #Artificial Intelligence #Neural Networks #Deep Learning #Natural Language Processing (NLP) #Recurrent Neural Networks (RNN) #Long short-term memory (LSTM)