Last updated on 2021-06-23 at 18:59:36
This web page is the official web page for the MLRF course taught at EPITA. Students should find listed here (almost) all the resources they need for this course.
Teachers: Joseph Chazalon (lectures + practice), Olivier Ricou (practice), Julie Rivet (practice), Yizi Chen (practice).
Students’ weekly workflow should be:
We will try to use the right tool for the right job. Here are the tools and their job(s).
You need to set up a programming environment on your computer to complete this course. We tried to make this as easy as possible.
We use Jupyter, a notebook interface for scientific Python. We provide you with notebooks that you have to edit to complete each practice session.
To get a working environment, you first need to install a recent (3.7+) Python environment. Then, you need to install some packages to get all the necessary tools We recommend using pip
, the Python package installer, as follows;
pip install --user \
jupyter \
matplotlib \
numpy \
opencv-contrib-python-headless \
scikit-image \
scikit-learn
You may need to edit your $PATH
and add $HOME/.local/bin
to the list of paths.
Then, you should be able to navigate to a directory containing Jupyter Notebooks (.ipynb
files) and start a Jupyter server using
jupyter notebook
This should automatically launch a browser and let you interact with notebooks. We recommend that you read those two resources to get Jupyter’s basics:
Course resources are available on the AFS at the following mount point: /afs/cri.epita.fr/resources/teach/bigdata/mlrf21
To save your Jupyter and IPython configurations you may want to use the following commands:
mkdir -p ~/afs/.confs/jupyter/
mkdir -p ~/afs/.confs/ipython/
ln -sf ~/afs/.confs/jupyter/ ~/.jupyter
ln -sf ~/afs/.confs/jupyter/ ~/.ipython
Remarks: