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Entry to mid-level data scientists / machine learning engineers / ...
This workshop introduces the participants to the topic of anomaly detection. Inparticular, it provides initial answers for the following questions:
Successful participants will acquire the basic theoretical knowledge and the practical skills to perform anomaly detection on simple use cases using state-of-the-art methods. While mostly covering introductory material, the workshop tries to provide enough depth to be interesting for participants with some experience in anomaly detection.
We try to keep the prerequisites as low as possible. Some experience with standard machine learning methods and basic knowledge of the python machine learning stack are however highly recommended.
Besides setting up the environment yourself, we provide a devcontainer that can either be used locally or inside a GitHub Codespace. To quickly spin up an instance, holding the training's content and the necessary environment, click the green button "Code" in the top right corner of the repository and select "Codespaces" rather than local development.
If you prefer to work locally, you can set up the environment as follows:
We recommend to install rise with conda (installation with pip may cause problems). We also use the spellchecker and equation-numbering extensions.
To configure everything, activate a conda env and run
conda install -c conda-forge notebook rise jupyter_contrib_nbextensions jupyter_nbextensions_configurator conda install -c conda-forge ffmpeg python ./configure_spellcheck_dict.py jupyter nbextension enable spellchecker/main jupyter nbextension enable equation-numbering/main
Use the extension-configurator for customizing your slideshow as described here.
The hide_code extension is useful to see the slides in presentation mode. You can install it by typing the following lines
pip install hide_code jupyter nbextension install --py hide_code jupyter nbextension enable --py hide_code jupyter serverextension enable --py hide_code
Finally, clone repository, change into the directory of the cloned repository and type
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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