Curated from: scotthyoung.com
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Say you need to learn a complicated skill: physics, French or computer programming. How much time should you spend building your background knowledge before you start practicing the actual skill?
Consider machine learning. One way to learn this field would be to master first the underlying math. Then, when you encountered the programming commands for certain mathematical functions, you’d know what they are doing behind the scenes.
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At a basic level, we can contrast two ways of learning. One way is to memorize, by rote, the answer to every possible question in a domain. The other way is to learn a process for generating solutions in the domain.
Children seem to do exactly this when learning arithmetic. They begin with a procedure, like counting. As they gain experience, they memorize more and more of the exact answers. Eventually, they are able to solve most arithmetic problems through recall alone, and the procedure of counting fades away.
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When performing skills, we use a variety of methods, from following a procedure to retrieving an answer from memory. With increased practice, the memory component becomes dominant for routine situations. Even when we can’t get the exact solution from memory, we may find parts of the answer which we can use to solve the problem faster.
Digging deeper has two benefits:
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