Blog
Self-published2021This post adapts test-time augmentation, a computer vision staple, to tabular data. We benchmark strategies for numeric and categorical features and measure how much accuracy they actually buy at inference.
Self-published2021Spreadsheets stop scaling once an experiment log runs to hundreds of rows. We move tracking onto Neptune.ai across a competition-style workflow, logging metrics, parameters, and artifacts, then comparing runs to decide what to keep.
Self-published2021Large models often will not fit a useful batch size into GPU memory. We use gradient accumulation to reach a large effective batch anyway, walking through the modified training loop and its interaction with learning rate scaling and batch norm.
Self-published2020When labeled training data is scarce but unlabeled test data is plentiful, the test set is still useful. We pre-train on surrogate labels derived from it, then fine-tune on the real labels to lift network performance.
Self-published2020We forecast retail demand with gradient boosted trees in our Data Mining Cup 2020 solution. Optimizing a profit-based objective rather than plain forecast error aligns the model with the underlying inventory cost and revenue trade-offs.
Self-published2020A convolutional network that grades diabetic retinopathy severity from retina photographs, built for a Kaggle competition and the Udacity ML Engineer program. We cover retina-specific preprocessing, augmentation, and the ordinal grading setup.