Plan Your Broad-Strokes Journey Top-Down, But Carry Out the Granular Steps Bottom-Up
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The top-down approach can be useful for planning a broad-strokes learning journey towards a goal. For instance, if you want to learn machine learning, then you can think top-down to figure out what fields of math you need to learn in order for machine learning to become accessible to you. Youâll find that you absolutely need to learn calculus, linear algebra, and prob/stats, and you can skip stuff like abstract algebra, number theory, etc.
However, the granular steps of the journey, the actual learning, needs to be carried out bottom-up. For instance, are you really going master computing neural net weight gradients via backpropagation by asking âwhat does that squiggly âdâ mean,â âwhy do you have to chain-multiply the derivatives like that,â âhow do you calculate the derivative of any activation function,â etc., all the way down to the depths of whatever is the last piece of math youâve mastered?
No, all youâre going to do with those questions is create a roadmap of what you need to learn. Which is essentially a calculus course. Except your roadmap will be terrible because you donât actually know the subject yourself â it will have all sorts of gaps that you donât even realize are missing because, which is to be expected given that you donât actually know the subject.
Youâll try to climb back up the skill tree implied by your incomplete roadmap and youâll repeatedly get stuck trying to climb up to the next branch that you canât reach because there are prerequisites that you donât realize youâre missing.
Most people in this situation will eventually just give up due to all the friction. Only those who have extremely outsized perseverance and generalization ability have any chance of fighting through and making it to the other side. And even then, it will take longer (and theyâll likely end up with more holes in their knowledge) than if they just sucked it up and worked through a well-sequenced calculus course.
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