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VirtualHat t1_j9j8uvr wrote

Reply to comment by GraciousReformer in [D] "Deep learning is the only thing that currently works at scale" by GraciousReformer

For example, in IRIS dataset, the class label is not a linear combination of the input. Therefore, if your model class is all linear models, you won't find the optimal or in this case, even a good solution.

If you extend the model class to include non-linear functions, then your hypothesis space now at least contains a good solution, but finding it might be a bit more trickly.

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GraciousReformer OP t1_j9jgdmc wrote

But DL is not a linear model. Then what will be the limit of DL?

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terminal_object t1_j9jp51j wrote

You seem confused as to what you yourself are saying.

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GraciousReformer OP t1_j9jppu7 wrote

"Artificial neural networks are often (demeneangly) called "glorified regressions". The main difference between ANNs and multiple / multivariate linear regression is of course, that the ANN models nonlinear relationships."

https://stats.stackexchange.com/questions/344658/what-is-the-essential-difference-between-a-neural-network-and-nonlinear-regressi

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PHEEEEELLLLLEEEEP t1_j9k691x wrote

Regression doesnt just mean linear regression, if that's what you're confused about

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