conda install -c conda-forge neptune-client conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch conda install pytorch-lightning -c conda-forge conda install scikit-learn conda install -c plotly plotly conda install -c plotly plotly-orca
conda develop /home/pawel/PycharmProjects/calina
Run .envrc with source .envrc to add calina
path from this repository:
https://gitlab.cern.ch/mmajewsk/calina
https://misio.fis.agh.edu.pl/media/misiofiles/c54f16de7751c4a8525e63c45120c17d.pdf https://github.com/Czerwooonka/pracaMagisterska_WizualizacjaDanych https://www.researchgate.net/publication/336417452_Analysis_and_machine_learning_anomaly_detection_of_the_VELO-LHCb_calibration
Part of the project, concerning Autoencoders and PCA, was presented by Tymoteusz Ciesielski and Paweł Drabczyk on ICPS 2021 International Conference of Physics Students in Copenhagen. Below you can find the slides.