Master TAL - MSc. NLP
This course provides a comprehensive view of machine learning and neural networks,
starting from multilayer perceptrons to advanced architectures currently used
(deterministic/probabilistic networks, convolutive networks (CNN), and recurrent networks (RNN).
We survey the fundamentals of neural network algorithms, and we introduce several properties that aid in the selection of the most appropriate architectures of networks depending on the task at hand.
- Expertise in neural network theories
- Application of these theories from problem-solving
- Analyse a problem before computationally treating spoken or written data
- Know how to apply algorithmic techniques, linguistic analysis, statistics, and knowledge processing.
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, « Deep Learning », https://www.deeplearningbook.org/
- Hugo Larochelle, « Online Course on Neural Networks », http://info.usherbrooke.ca/hlarochelle/neural_networks/
- Modern Deep Learning Techniques Applied to Natural Language Processing, https://nlpoverview.com/
- Tracking Progress in Natural Language Processing, http://nlpprogress.com/
- PyTorch Tutorials, https://pytorch.org/tutorials/
Course URL – Arche
Number of Tests
Number of the tests
- Written exam (1) and multiple choice exam (1)
- Software project (group of 2 people)
Combine with other specialization
- MSc Cognitive Science