PyTorch examples and presentation (pdf) shown in the talk "End-to-end Modelling and Optimization of Optical Communication Systems using Deep Learning" presented at the "AI for Optical Networks & Neuromorphic Photonics for AI Acceleration" summer school at Fraunhofer HHI
In this repository, you can find some of the examples that are shown in our presentation called "End-to-end Modelling and Optimization of Optical Communication Systems using Deep Learning" presented at the "AI for Optical Networks & Neuromorphic Photonics for AI Acceleration" summer school at Fraunhofer HHI. Authors are Laurent Schmalen, Boris Karanov and Vincent Lauinger.
Additionally, you can find the slides accompanying the presentation (for background information) in the root directory.
The programming language Python is usually pre-installed in current Linux distributions and OSX. Additionally required modules need to be installed by hand from the packet sources. Alternatively, we highly recommend to use readily available Python distributions that are tuned for data science. One such distribution is Anaconda. Anaconda is also the preferred method to install a complete Python environment on a Windows machine. If you are using Anaconda, we advise you to create an environment within you run the notebooks. You can directly create the environment for running the notebooks using the provided environment.yml file using conda env create -f environment.yml. You can then activate the envinronment using conda activate PyTorch.
This work has received funding from the European Research Council (ERC) under the European Union's Horizon2020 research and innovation programme (grant agreement No. 101001899).