if nobody bites on question 3, it will make my next medium post a lot easier to write :)https://twitter.com/GaryMarcus/status/1209640096900812800 …
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When most or all free parameters in the system are being learned by backprop-enabled gradient descent, that’s deep learning.
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Your definitions are good but I prefer clusters induced by definitions to be context dependent. For example, I can have a giant network where each function is the dot product & each edge a weight. It would be differentiable but it can be rewritten as a single linear transform.
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