Test your knowledge

Now that you have completed the recipes in this chapter, you can follow the next steps to exercise that you have learned:

1. Make a copy of the mnist_hello_world.ipynb notebook—call it mnist_hello_world_variations.ipynb.
2. Update your new copy of the notebook to ingest a variation of the MNIST dataset, called MNIST_SAMPLE. Which statement will you need to update to ingest this dataset rather than the full-blown MNIST curated dataset?
3. Use the path.ls() statement to examine the directory structure of the MNIST_SAMPLE dataset. How is the output of this statement different from its output for the full-blown MNIST dataset?
4. Keeping in mind the difference in the directory structure of the MNIST_SAMPLE dataset, update the values of the train and valid parameters in the following statement so that it will work with this dataset:

   dls = ImageDataLoaders.from_folder(path, train='training', valid='testing')

5. Again keeping the directory...