Problem library
ML building blocks, one implementation at a time.
Practice the functions, model classes, and training loops inside production ML systems.
Compare Two Datasets
Debug deterministic schema and aligned-row comparisons between datasets.
Detect Null Drift
Flag suspicious changes in feature null rates.
Feature Distribution Comparison
Debug fixed-bin PSI calculation and drift classification.
Label Distribution by Segment
Debug segment-local label distributions with stable dense output axes.
Calibration by Bucket
Debug a per-bin calibration table while preserving gaps and empty buckets.
Confusion Matrix by Group
Debug deterministic grouped TN, FP, FN, and TP counts.
PyTorch Contrastive Loss
Debug a mini-batch query-document contrastive trainer with in-batch negatives.
PyTorch Streaming Softmax
Compute stable softmax probabilities from chunked logits with an online max and denominator.