Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris
This course is being taught at as part of Master Year 2 Data Science IP-Paris
The course covers the basics of Deep Learning, with a focus on applications.
Note: press "P" to display the presenter's notes that include some comments and additional references.
The Jupyter notebooks for the labs can be found in the labs
folder of
the github repository:
git clone https://github.com/m2dsupsdlclass/lectures-labs
These notebooks only work with keras and tensorflow
Please follow the installation_instructions.md
to get started.
Direct links to the rendered notebooks including solutions (to be updated in rendered mode):
This lecture is built and maintained by Olivier Grisel and Charles Ollion
Charles Ollion, head of research at Heuritech - Olivier Grisel, software engineer at Inria
We thank the Orange-Keyrus-Thalès chair for supporting this class.
All the code in this repository is made available under the MIT license unless otherwise noted.
The slides are published under the terms of the CC-By 4.0 license.