Comment on Every Model Learned by Gradient Descent Is Approximately a Kernel MachineparentComments−api5yThere is probably a ton of isomorphism between different models. It may come down to what is easiest to understand and fastest to implement in code.−segfaultbuserr5ySee also:A visual proof that neural networks can approximate any functionhttps://news.ycombinator.com/item?id=19708620−api5ySo a "neural network" is actually a type of parameterized mathematical function that can be fit to any curve including higher dimensional surfaces, etc.?−xksteven5yThat's correct. See https://en.wikipedia.org/wiki/Universal_approximation_theore... for more details−laingc5yYes.
Comments
There is probably a ton of isomorphism between different models. It may come down to what is easiest to understand and fastest to implement in code.
See also:
A visual proof that neural networks can approximate any function
https://news.ycombinator.com/item?id=19708620
So a "neural network" is actually a type of parameterized mathematical function that can be fit to any curve including higher dimensional surfaces, etc.?
That's correct. See https://en.wikipedia.org/wiki/Universal_approximation_theore... for more details
Yes.