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If one specific tool / approach seems unreasonably effective, it's usually because the problems you've been trying to solve all belong to the same category. You may not expect the same effectiveness on new kinds problems.
If all you've been doing so far is repairing rooftops and unclogging roof gutters, you may think ladders are all you need. Until you face a plumbing problem. Deep learning (differentiable parametric functions trained with gradient descent on large datasets) is exactly like that.
The people trying to fix plumbing issues using ladders are slightly less visionary than they think.
Which is why it’s important to have many tools. Otherwise, frustration comes easy. Coders already do this and build up patience over time. They use a series of tools and if none work, they create one.
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