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What kinds of tasks/domains does fastai provide high-level support for, beyond just image classification?
Questions in this quiz
What kinds of tasks/domains does fastai provide high-level support for, beyond just image classification?
What is a `DataLoaders` object in fastai, and what is its role?
What is the significance of fastai being built on top of PyTorch specifically, rather than being an entirely independent library?
What is fastai's "data block API," and what problem does it solve for setting up a training pipeline?
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 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 "transfer learning," and how does fastai commonly make it accessible through functions like `vision_learner`?
What is fastai?
What is the purpose of the "learning rate finder" (`lr_find()`) in fastai?
What is the "fastai philosophy" often described regarding making deep learning "accessible," and how does this manifest in the library's design choices?
What is a fastai "Learner", and what is its role?
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?