Day 2 competition log covering ensemble strategies (bagging vs. boosting) and CNN model selection for image classification.

Tips on efficient data feeding, transform ordering for performance, and using Albumentations for image augmentation.

Image preprocessing and data augmentation techniques for deep learning competitions, including resizing, bounding box cropping, and torchvision transforms.

Training plan for a mask classification competition: per-feature models with ensemble learning, plus Jupyter vs. CLI workflow notes.

Guide to approaching AI competitions with EDA strategies for image classification tasks.

Using matplotlib facets with GridSpec and subplots to visualize datasets from multiple perspectives.