Welcome to the webpage of the Deep Learning course. This course is taught by Professor Stéphane Gaïffas. This course is for students from the Masters 2 programs MIDS and M2MO. On this webpage you will find all the teaching material (mainly lecture slides, jupyter notebooks and code for the labs).
The course takes place remotely on Zoom here:
on mondays 14:00 - 17:00, the agenda is here : https://m2mids.github.io/m2mids/edt/
Go and log in the following
Slack space (ideally install
Slack on your laptop):
if you haven’t already. You can ask your question in the
Agenda for the course
Week 1Lecture: Course logistics and the success to deep learning. Lab: Polynomial logistic regression versus multi-layer perceptron on toy datasets.
Week 2Lecture: Machine learning recap, history of neural networks and the main building blocks. Lab: Quick description of PyTorch tensors.
Week 3Lecture: Some hyperparameters, regularization techniques and practical recommendations. Lab: Using pre-trained neural networks for complex tasks.
Week 4Lecture: About optimization and optimizers for deep learning. Lab: Homemade perceptron on toy-data and multi-layer feedforward neural net on CIFAR-10 using PyTorch.
Week 5Lecture: Convolutional neural networks or how to make a neural net see ? Lab: Convolutional neural networks in PyTorch.
TODO: update the agenda along the weeks
Here is a list of learning ressources that can be useful for this course, among many others.
- Deep learning book about the main concepts
- Francois Chollet’s book about
- Aurélien Géron’s book about general machine learning with
- Jeremy Howard’s book and
The course will focus mainly
tensorflow for deep learning, and we will use the regular
Python stack for data-science, namely
pandas, among some others stuff.