This webpage provides access to slides, recordings, and a virtual machine for François Fleuret’s deep learning courses at the University of Geneva, Switzerland. The course covers a comprehensive introduction to deep learning using examples in the PyTorch framework. The materials include lectures on machine learning objectives, tensor operations, automatic differentiation, gradient descent, and specific deep learning techniques such as generative, recurrent, and attention models. The course was initially developed at the Idiap Research Institute and taught at the École Polytechnique Fédérale de Lausanne. In addition to the resources available on the website, the author also offers “The Little Book of Deep Learning,” a short introduction to deep learning for readers with a STEM background. The webpage provides access to slide PDFs, handout PDFs, and screencasts for each lecture, as well as practical sessions and prologue files. The prologue files include helper functions for the practical sessions, such as data loading and argument parsing. The webpage also offers a virtual machine that simulates a complete computer with a Linux operating system and the necessary tools for using PyTorch on a web browser. The virtual machine can be accessed through a web browser on the host machine and includes JupyterLab,
https://fleuret.org/dlc/