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Enseignement scientifique & technique - IA327 : Sequence-to-Sequence Models for NLP and Speech Processing

Domaine > Image-Données-Signal.

Descriptif

Natural language processing has given rise to innumerable industrial applications.
While many new tasks have emerged in NLP and speech processing over
the last decades, methods to solve them have increasingly converged towards
a unified modeling paradigm. In this course, we will use sequence-to-sequence
modeling to delve into state-of-the-art statistical machine learning methods —
convolutional neural networks, recurrent neural networks, attention, transformers
— and apply them to major NLP and speech processing tasks — language
modeling, machine translation, speech recognition, information extraction. Students
should expect to get an in-depth understanding of these methods, through
theoretical analysis and hands-on lab sessions. Grading will involve a project,
to be carried out over the course of the class.
Topics to be covered
1. Recurrent Neural Networks
2. Hidden Markov models
3. Attention Mechanisms
4. Transformers
5. Convolutional Neural Networks
6. Language Modeling

Format des notes

Numérique sur 20

Littérale/grade américain

Pour les étudiants du diplôme Echange international non diplomant

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2 ECTS

Pour les étudiants du diplôme Diplôme d'ingénieur

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2 ECTS
  • Crédit d'Option 3A acquis : 2

Pour les étudiants du diplôme Programme de mobilité des établissements français partenaires

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2 ECTS
  • Crédit d'Option 3A acquis : 2

Pour les étudiants du diplôme Data & Artificial Intelligence

Le rattrapage est autorisé (Note de rattrapage conservée)
    L'UE est acquise si Note finale >= 10
    • Crédits ECTS acquis : 2.5 ECTS

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