Tutorial 3
Machine Learning on GPUs
These notebooks apply GPU-native ML workflows to tabular prediction. Participants train and interpret cuML logistic regression, then execute XGBoost with GPU settings, train-test split strategy, model inspection, and prediction quality evaluation using ROC and AUC. A CPU comparison path highlights practical performance differences and trade-offs.
- cuML logistic regression and explainability-style checks
- GPU XGBoost training/inference/evaluation, optional CPU comparison
What you will do
- open a ready-to-run Colab notebook
- work through a GPU-based machine learning training workflow
- experiment with tuning and evaluation in an interactive environment
- adapt the notebook for follow-up research projects