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In this video Rachel Thomas provides an interesting take on learning ML: instead of promoting the typical bottom-up approach, fast.ai promotes a top-down approach. From a pedagogical perspective this seems counter intuitive. Surely you need to know the building blocks before you can move on to the theory that builds on the building blocks? Indeed, that is how traditional education proceeds. However, when consultants provide feedback to executives they tend to take a top-down approach. Why is that?
The main reason for taking a top-down approach when writing up/presenting technical findings is that you can provide a roadmap for where you are heading. This means that when you step into the details, the stakeholders can, because they now have a map of where you are heading, know how the details relate to the bigger picture. This is precisely why I think the fast.ai approach to learning ML can be effective. Rachel Thomas provides further motivation for their approach in their video: How to Learn Deep Learning (when you’re not a computer science PhD)