Build an end-to-end image classification pipeline on CIFAR-10: dataset, augmentation, model, training loop, evaluation
Explain the role of each component (dataloader, loss, optimizer, scheduler, augmentation) and predict how breaking any one of them manifests in the loss curve
Implement mixup, cutout, and label smoothing from scratch and justify when each is worth adding
Read a confusion matrix and a per-class precision/recall table to diagnose dataset and model failures beyond aggregate accuracy
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