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.
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