This section of the ML-INFN Confluence Space contains the Knowledge Base of fully implemented use cases. This has been created in order to provide new users getting close to Machine learning with concrete examples, with step by step guides for reproducibility.
The division into categories is multidimensional
- Dimension 2: per Machine Learning technology (CNN, Auto encoders, LSTM, GraphNet, ...)
- Dimension 1: per scientific field (High Energy Physics, Gravitational Waves, Medical Physics, ...)
- Dimension 3: per type of used tool
and is implemented via Confluence labels.
Table of Use cases
| Name and Link | ML Technologies | Scientific Field | ML Tools | Comments |
|---|---|---|---|---|
| B-tagging at CMS | 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 |