Deep Learning Course

You can find here slides, recordings, and a virtual machine for François Fleuret's deep-learning courses 14x050 of the University of Geneva, Switzerland.

This course is a thorough introduction to deep-learning, with examples in the PyTorch framework:

You can check the pre-requisites.

The cover of the Little Book of Deep Learning

In addition to the materials available here, I also wrote and distribute "The Little Book of Deep Learning", a phone-formatted short introduction to deep learning for readers with a STEM background.

This course was developped initialy at the Idiap Research Institute in 2018, and taught as EE-559 at École Polytechnique Fédérale de Lausanne until 2022. The notes for the handouts were added with the help of Olivier Canévet.

Thanks to Adam Paszke, Jean-Baptiste Cordonnier, Alexandre Nanchen, Xavier Glorot, Andreas Steiner, Matus Telgarsky, Diederik Kingma, Nikolaos Pappas, Soumith Chintala, and Shaojie Bai for their answers or comments.

Lecture materials

The slide pdfs are the ones I use for the lectures. They are in landscape format with overlays to facilitate the presentation. The handout pdfs are compiled without these fancy effects in portrait orientation, with additional notes. The screencasts are available both as in-browser streaming or downloadable mp4 files.

You can get archives with all the pdf files (1107 slides):

and subtitles for the screencasts generated automaticallly with OpenAI's Whisper:

or the individual lectures:





You may have to look at the Python, Jupyter notebook, and PyTorch documentations at

Practical session prologue

Helper Python prologue for the practical sessions:

Argument parsing

This prologue parses command-line arguments as follows

usage: [-h] [--full] [--tiny] [--seed SEED]
[--cifar] [--data_dir DATA_DIR]

DLC prologue file for practical sessions.

optional arguments:
-h, --help           show this help message and exit
--full               Use the full set, can take ages (default
--tiny               Use a very small set for quick checks
(default False)
--seed SEED          Random seed (default 0, < 0 is no seeding)
--cifar              Use the CIFAR data-set and not MNIST
(default False)
--data_dir DATA_DIR  Where are the PyTorch data located (default
$PYTORCH_DATA_DIR or './data')

Loading data

The prologue provides the function

load_data(cifar = None, one_hot_labels = False, normalize = False, flatten = True)

which downloads the data when required, reshapes the images to 1d vectors if flatten is True, and narrows to a small subset of samples if --full is not selected.

It returns a tuple of four tensors: train_data, train_target, test_data, and test_target.

If cifar is True, the data-base used is CIFAR10, if it is False, MNIST is used, if it is None, the argument --cifar is taken into account.

If one_hot_labels is True, the targets are converted to 2d torch.Tensor with as many columns as there are classes, and -1 everywhere except the coefficients [n, y_n], equal to 1.

If normalize is True, the data tensors are normalized according to the mean and variance of the training one.

If flatten is True, the data tensors are flattened into 2d tensors of dimension N × D, discarding the image structure of the samples. Otherwise they are 4d tensors of dimension N × C × H × W.

Minimal example

import dlc_practical_prologue as prologue

train_input, train_target, test_input, test_target = prologue.load_data()

print('train_input', train_input.size(), 'train_target', train_target.size())
print('test_input', test_input.size(), 'test_target', test_target.size())


* Using MNIST
** Reduce the data-set (use --full for the full thing)
** Use 1000 train and 1000 test samples
train_input torch.Size([1000, 784]) train_target torch.Size([1000])
test_input torch.Size([1000, 784]) test_target torch.Size([1000])

Virtual Machine

A Virtual Machine (VM) is a software that simulates a complete computer. The one we provide here includes a Linux operating system and all the tools needed to use PyTorch from a web browser (e.g. Mozilla Firefox or Google Chrome).


  1. Download and install Oracle's VirtualBox,
  2. download the virtual machine OVA package (1.68Gb), and
  3. open the latter in VirtualBox with File → Import Appliance.

You should now see an entry in the list of VMs. The first time it starts, it provides a menu to choose the keyboard layout you want to use (you can force the configuration later by running the command sudo set-kbd).

If the VM does not start and VirtualBox complains that the VT-x is not enabled, you have to activate the virtualization capabilities of your CPU in the BIOS of your computer.

Using the VM

The VM automatically starts a JupyterLab on port 8888 and exports that port to the host. This means that you can access this JupyterLab with a web browser on the machine running VirtualBox at http://localhost:8888/ and use Python notebooks, view files, start terminals, and edit source files. Typing !bye in a notebook or bye in a terminal will shutdown the VM.

You can run a terminal and a text editor from inside the Jupyter notebook for exercises that require more than the notebook itself. Source files can be executed by running in a terminal the Python command with the source file name as argument. Both can be done from the main Jupyter window with:

This VM also exports an ssh port to the port 2022 on the host, which allows to log in with standard ssh clients on Linux and OSX, and with applications such as PuTTY on Windows. The default login is 'dave' and password 'dummy', same password for the root account.


Note that performance for computation will be very poor compared to installing PyTorch natively on your machine. In particular, the VM does not take advantage of a GPU if you have one.

Finally, please also note that this VM is configured in a convenient but highly non-secured manner, with easy to guess passwords, including for the root, and network-accessible non-protected Jupyter notebooks.

This VM is built on a Linux Debian, with miniconda, PyTorch, MNIST, CIFAR10, and many Python utility packages installed.

License of use

My own materials on this page are licensed under the Creative Commons BY-NC-SA 4.0 International License.

More simply: I am okay with this material being used for regular academic teaching, but definitely not for a book / youtube loaded with ads / whatever monetization model I am not aware of.