Color usage in data visualization: sequential and diverging colormaps, HSI color space, and color palettes.

Common PyTorch troubleshooting tips for GPU memory issues, OOM errors, tensor accumulation, and inference best practices.

Overview of hyperparameter tuning methods including grid search, random search, and Bayesian optimization, with an introduction to Ray for parallel tuning.

Quick tip on displaying training accuracy and loss inside tqdm progress bars in PyTorch.

Week 3 of Naver Boostcamp AI Tech covering PyTorch custom models, datasets, dataloaders, and data visualization.

Guide to multi-GPU training in PyTorch covering model parallelism, DataParallel, and DistributedDataParallel with code examples.