Course

EE-608: Deep Learning For Natural Language Processing

Summary

This course covers advanced topics in deep learning architectures for natural language processing. The focus is on attention-based architectures, structure processing and variational-Bayesian approaches, and why these models are particularly suited to the properties of human language.

Lectures

Instructor

James Henderson

Teaching & PhD PhD Students Haruki Shirakami, Dina El Zein, Jose Rafael Espinosa Mena, Kimia Alavi, Mojtaba Nafez Past EPFL PhD Students as codirector Lesly Sadiht Miculicich Werlen, Rabeeh Karimi Mahabadi, Alireza Mohammadshahi, Andreas Thomas Marfurt, Jan Frederik Jonas Florian Mai, Melika Behjati, Fabio James Fehr, Andrei Catalin Coman Courses Deep Learning For Natural Language Processing EE-608 This course covers advanced topics in deep learning architectures for natural language proce

Official source

https://edu.epfl.ch/coursebook/en/deep-learning-for-natural-language-processing-EE-608

Moodle Page

http://go.epfl.ch/EE-608

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Summary

Lectures

Instructor

James Henderson

Official source

https://edu.epfl.ch/coursebook/en/deep-learning-for-natural-language-processing-EE-608

Moodle Page

http://go.epfl.ch/EE-608

About this result

Lectures in this course (5)

Binary Sentiment Classifier Training

Covers the training of a binary sentiment classifier using an RNN.

Pitfalls in Empirical NLP Research

Explores pitfalls in NLP research, emphasizing the importance of standardized metrics and statistical testing.

NLP Pre-processing: Tokenization, Stop Words, Lemmatization

Covers tokenization, stop words removal, and lemmatization for NLP tasks.

Graph-to-Graph Transformers: Syntax-aware Graph Encoding

Introduces the Syntax-aware Graph-to-Graph Transformer architecture for effective conditioning on syntactic dependency graphs.

Seq2Seq Models: Attention vs. No Attention

Explores Seq2Seq models with and without attention mechanisms, covering encoder-decoder architecture, context vectors, decoding processes, and different types of attention mechanisms.

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