Use when producing a runnable deliverable for an ML task, to decide the target environment and author the notebook or script. Detects whether the user wants a Kaggle notebook, a Google Colab notebook, or a local GPU run, and adapts paths, data loading, and compute settings accordingly. Also enforces the writing style…
Use when evaluating or reporting model performance and choosing metrics, thresholds, and plots that FIT the problem type. Picks the right metrics per task (binary uses ROC-AUC and PR-AUC; multiclass uses macro F1 and a confusion matrix; detection uses mAP; segmentation uses Dice and IoU; regression uses MAE, RMSE, R2…
Use when training or fine-tuning a deep model and tuning it for best accuracy. Covers transfer learning, two-phase fine-tuning, discriminative/layer-wise LR, LR schedules + warmup, regularization tuned to over/under-fit (dropout, weight decay, label smoothing, MixUp), mixed precision, gradient checkpointing, EMA…