1/ Random thought: Deep Learning model development has seemed to me to be a lot like cooking. Typical example: Images looking a little strange here? Train on this other loss to tone it down a bit. How much? Until it's done!
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10/ This is in contrast to the bottom up method that you see in fields like math, where the reward of actually using it practically and intuitively never seems in your grasp until years down the road.
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11/ But another key component is simply bludgeoning yourself with hard earned experience. You can't help but pick up on insightful patterns if you keep paying attention to what you think is going to happen vs what actually happens in your models.
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12/ I think this is pretty similar to what Malcolm Gladwell describes in the book "Blink", which famously describes the now somewhat derided 10,000 hour rule of achieving expertise.
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13/ The definition of expertise in that book though I think is great: That you start as a novice by carefully overthinking every step and sticking to "recipes" without necessarily understanding the tasks of a field when first starting out.
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14/ But then gradually, you switch from the "recipes" to more of a reliance on "pattern recognition". Pattern recognition, or "intuition", seems to be developed in a way that seems analogous to neural networks: You just have to keep incrementing with good real world feedback!
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