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The division into categories is multidimensional
- Dimension 21: per Machine Learning technology (CNN, Auto encoders, LSTM, GraphNet, ...)
- Dimension 12: per scientific field (High Energy Physics, Gravitational Waves, Medical Physics, ...)
- Dimension 3: per type of used tool
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| Name and Link | ML Technologies | Scientific Field | ML Tools | Comments |
|---|---|---|---|---|
| Btagging in CMS (templated version) | CNN, LSTM | High Energy Physics | Keras + Tensorflow | Realistic application |
| LHCb Masterclass, with Keras | DE, MLP | High Energy Physics | ROOT + Keras + TF | Introductory tutorial |
| MNIST in a C header | MLP | Keras | Free-styling tutorial | |
| CNN, RNN, GNN | High Energy Physics | PyTorch | Package use examples | |
| INFERNO: Inference-Aware Neural Optimisation | NN | High Energy Physics | Keras + Tensorflow | Technique application example |
| An introduction to classification with CMS data | Fisher, BDT, MLP | High Energy Physics | Scikit-learn, TF2 | Tutorials for Master Students |
| Virgo Autoencoder tutorial | Autoencoder | General Relativity | Python Keras | Tutorial for student |
| Distributed training of neural networks with Apache Spark | DNN | High Energy Physics | Spark + BigDL | Tutorial |
| FTS log analysis with NLP | NLP | High Energy Physics, Computing | Word2Vec + Rake + sklearn | |
Image Inpainting tutorial: how to digitally restore damaged images | CNN U-Net | Applied Physics | Keras + Sci-kit image, PIL, OpenCV, matplotlib | Tutorial |
| ANNs, RF | High Energy Physics | Keras , TensorFlow, Scikit-learn | Tutorial | |
| CNN | Medical Physics | Keras, Tensorflow | Tutorial |
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Once you finish with the creation of the page don't forget to edit the page "33161472Machine Learning Knowledge Base" (this same page!) and add the use case in the "Table of Use cases".
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