M2MO: Modélisation Aléatoire, Finance et Data Science

Master en statistique, probabilités et finance - Université Paris 7 - Paris Diderot

Courses Group Data Science Deep learning

Deep learning

Lecturer: S. Gaiffas
Term  3
Schedule: 2 hours of lecture  per week


  1. Introduction to Deep Learning
  2. Forward and backward propagation and solvers
  3. Embeddings, matrix factorization, factorization machines and recommender systems
  4. Convolutional neural networks for image classification
  5. Network architectures for object detection and image segmentation
  6. Recurrent neural networks, Long Short-Term Memory (LSTM) units for learning based on sequences
  7. Learning for sequences to sequences, attention and memory
  8. Unsupervised deep learning and generative models


  • Goodfellow, I. and Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press.
  • Chollet, F. and Allaire, J. J. (2018). Deep Learning with R. Manning Pub.
  • Chollet, F. and Allaire, J. J. (2018). Deep Learning with Python. Manning Pub.