Ensemble methods for AI competitions: hard voting, soft voting, and weighted voting to improve model performance.

How gradient accumulation works in PyTorch for effective large-batch training on limited GPU memory.

Practical training techniques including AMP, label smoothing, ArcFace loss, class pivot adjustment, and Wandb logging for image classification.

Clarifying common mistakes in PyTorch training loops, validation ordering, and K-fold cross validation usage.

Applying vertical CutMix augmentation for face mask classification to focus patches on facial regions.

CutMix data augmentation in PyTorch: loss computation, accuracy, and F1 score calculation.