Introduction to Deep Learning, ESPCI

This course is an introduction to deep-learning approach with lab sessions in pytorch (python module). The goal is to understand the data-driven approach and to be able to efficiently experiment with deep-learning on real data.


  • Homework assignement 2: see the drive, for the notebooks.
    • First follow the notebook called idl-pytorch-starter.ipynb
    • Then the assignment is also a notebook : hw-2-idl-espci.ipynb
  • The deadline is the 3rd of February morning, before the lab session.
  • Next lab session: the 3rd of February, pytorch

The resources / drive

Look at this drive for the slides and the material of lab sessions.

Expected schedules

It starts in january 2020 (the 6th). The course are scheduled on monday, starting at 8:30 in the morning.

The first part:

  • 6/01, course: introduction and basics on machine learning
    • The main tasks
    • Objective function
    • Optimisation with gradient descent
  • 13/01, lab session : machine learning and python, first steps
    • numpy and matplotlib
    • logistic regression by hand
  • 20/01, course on feed-forward neural networks
    • Multi-class classification
    • From linear to non-linear classification
    • The feed-forward architecture and the back-propagation algorithm
  • 27/01, course on deep-learning
    • Deep networks
    • Drop out, regularization and other tricks
    • pytorch overview
  • 03/02, lab session : deep-learning in pytorch
  • 10/02, course: A first review, good practices in deep-learning, problem design

The second part:

  • 02/03, course: Convolutional and recurrent deep-networks
  • 09/03, lab session: Image and sequence processing
  • 23/03, course: Advanced architecture - part 1
  • 30/03, course: Advanced architecture - part 2