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
Lab Session
Word2Vec Implementation (with Maximum Log-Likelihood)
References
- pytorch tutorials
- pytorch documentation
- Natural Language Processing with PyTorch
Christopher Manning Lectures : 1 and 2
Session 2: Word Vectors Follow-up
Lab Session
Word2Vec Implementation (with Maximum Log-Likelihood)
References
- pytorch tutorials
- pytorch documentation
- Natural Language Processing with PyTorch
Christopher Manning Lectures : 1 and 2
Session 3: Convolutional Networks and Classification
Lab Session
Sentiment Analysis with CNNs
References
- Christopher Manning Lectures : course 11 and video
- https://arxiv.org/abs/1408.5882 one of the first paper describing such a system
- Webpage describing tuorial's dataset: https://deft.limsi.fr/2018/
Session 6: Pretraining
Lab Session
References
Remarks
- Evaluation: A quiz per week(starting second week)