Speaker
Liv Helen Vage
(Princeton University (US))
Description
Machine learning is advancing at a breathtaking pace, and navigating the ever-growing ecosystem of Python tools can be time consuming. This talk offers a practical guide to the ML landscape most relevant to high-energy physics. We discuss:
- Common ML frameworks including PyTorch, PyTorch Lightning, Keras, Jax, Scikit-learn - strengths and weaknesses and how to choose
- ML workflow tools including Weights & Biases, Mlflow, Optuna, b-hive - leverage tools to improve productivity and code quality
- Model training and deployment tools including hls4ml, Swan, HTCondor, ONNX - resources for training and scaling your models
- Supporting packages and structures including uproot, hist, awkward array - bridge HEP and ML workflows
- Industry tools: SageMaker, testing and linting, GitHub Actions - the differences between ML in HEP and industry, and what we can learn
- Fun shortcuts: make LLMs do your work, steal models from Hugging Face and other fun shenanigans
<brr>
Through live demonstrations, we will highlight practical strategies for adopting these tools with minimal friction, helping you up your ML game whether you’re new to the field or already deep in the weeds.
Author
Liv Helen Vage
(Princeton University (US))
