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What does `learn.show_results()` (or `learn.predict()`) commonly help a fastai user do after training a model?
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What does `learn.show_results()` (or `learn.predict()`) commonly help a fastai user do after training a model?
How does fastai's `tabular_learner` typically handle a mix of categorical and continuous (numeric) columns within a tabular dataset, without requiring the user to manually handle this distinction themselves?
What is the purpose of `interp = ClassificationInterpretation.from_learner(learn)` and its `plot_confusion_matrix()` / `plot_top_losses()` methods in fastai?
What is the purpose of a "callback" in fastai's training loop (e.g. `EarlyStoppingCallback`, `SaveModelCallback`)?
What is a fastai "Learner", and what is its role?
What is the purpose of splitting data into training and validation sets (a concept fastai's DataLoaders/DataBlock handles automatically via a specified `splitter`), and why is this distinction important when evaluating a model's actual performance?
What is the purpose of the fastai `Learner`'s ability to `export()` a trained model, and how does the resulting exported file typically get used?
What is a reasonable justification for fastai's emphasis on "sensible defaults" (e.g. `vision_learner` defaulting to a pretrained model, a reasonable learning rate schedule via `fine_tune()`, standard data augmentation) rather than requiring every parameter to be manually specified by the user?
What is a `DataLoaders` object in fastai, and what is its role?
What is "transfer learning," and how does fastai commonly make it accessible through functions like `vision_learner`?
What is fastai's "data block API," and what problem does it solve for setting up a training pipeline?
What is a reasonable comparison between fastai and using raw PyTorch (or another lower-level deep learning framework) directly, in terms of the tradeoff a practitioner faces?