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Some simple issues I found after a quick review:

• Typos
• Some paragraphs can be streamlined
• Some images are too large
• Some images seem unnecessary (e.g., the huge one on human inspection)

In research notebook for chapter 4, an ensemble of binary classifiers (for the MNIST classification problem) is created which achieves ~90% accuracy on the validation set.

However, the strategy used to train the individual classifiers uses a general random sampling, which makes it perfectly legitimate that some of the samples will contain more classes of a certain digit.

Would the validation accuracy rise if we sampled the corresponding proportion from each digit class instead?

This is a common practice in fastai notebooks and while it is probably harmless in the context of the course, I think it's a bad practice in general due to the potential for silent shadowing of function and variable names.

We should rewrite the notebooks to use the more commonly used practice of using a very short but common prefix. pandas uses pd, numpy uses np, seaborn uses sns, etc.

Some of the answers in the last section could use some correction in style and grammar.