Deep Learning has recently revolutionized natural language processing: translation, feature identification, dialogue systems, interpretation, etc.

Technical resources

Course material projected during training and sent to all trainees at the end of the course; case studies and practical examples chosen according to trainees' areas of interest

Performance monitoring

All trainees are asked to sign in every half-day Evaluation: Questionnaire to assess skills acquired at the end of the course

Assessment of results

Post-training satisfaction questionnaire

Pedagogical objectives

Theoretical courses mixed with examples and case studies. This course is designed to help you understand and implement these new techniques.

Technologies covered

Sequence to sequence, conditional random fields, Long Short Term Memory (LSTM), Convolutional Neural Network (CNN), Memory Networks, QANet, Transformer, BERT, Variational Auto-Encoders (VAE), Generative Adversarial Networks (GAN), PyTorch, TensorFlow

Target skills

Specific features of natural language processing

  • Dedicated deep architectures, state of the art
  • Model visualization and interpretation
  • Text generation