Natural Language Processing and Neural Networks

Table of Contents

Syllabus

This is a 6 part lecture. A new concept is presented in class at each session, before students can experiment with it directly during the programming tutorial.


Session 1: Introduction and Word Vectors 15/10/2026

Lecture

Introduction to NLP – Word Vectors : slides handoouts

Lab Session

Word2Vec Implementation (with Maximum Log-Likelihood)

Session 2: Word Vectors Follow-up 22/10/2026

Lecture

Introduction to NLP – Word Vectors : slides handoouts

Lab Session

Word2Vec Implementation (with Maximum Log-Likelihood)

Session 3: Convolutional Networks and Classification 28/10/2026

Lecture

Convolutional Networks for Text: slides handouts

Lab Session

Sentiment Analysis with CNNs

References


Session 4: Language Models 30/10/2026

Lecture

Definition of n-gram Language Models and Neural Language Models : slides and handouts

Lab Session

Language Models Language Models

References


Session 5: Transformers 13/11/2026

Lecture

Transformers and Self-Attention: slides and handouts

References


Session 6: Pretraining 26/11/2026

Lecture

Pretraining Transformers and Self-supervised Learning: slides and handouts

References

Remarks

  • Evaluation: A quiz per week(starting second week)

References

Author: Joseph Le Roux

Created: 2026-09-01 mar. 18:03