I know plenty of people that have built interesting software using the JPEG compressor, I know only a handful that would be able to explain to you in detail how the DCT powering it works and why it is able to achieve the compression ratio that it does. Obviously being able to understand all that will give you an advantage but for most 'mortals' jpeg_compress and jpeg_decompress is all they need to be productive.
fast.ai aims to bring a similar high level of understanding first and then backs that up with the basic principles of the theory being machine learning rounded off with a bunch of real world examples in code of classes of problems that you are likely to encounter.
I don't understand how your comment relates to mine. isn't it more or less what I said? you can use the frameworks and copy the examples like using a jpeg compressor for compressing what ever you want. But without the maths it is very unlikely you will go further (for example: not even going from a single label classifier to a multilabel).
About fast.ai, I would want to know those basic principles about deep learning and neural networks. I don't see them so basic unless you have a good background in algebra, optimization, statistics and the scientific method.
fast.ai aims to bring a similar high level of understanding first and then backs that up with the basic principles of the theory being machine learning rounded off with a bunch of real world examples in code of classes of problems that you are likely to encounter.