What are the situations that the bias for the hierarchy is not helpful?

So it is like an ability to capture "correlations" that cannot be done with random forests.

Still, why is it that DL has better inductive biases than others?

>inductive biases

Then why does DL have inductive biases and others do not?

So it works for images but not for tabular data?

But then what is the difference from the result that NN works better for ImageNet?

This is a great point. Thank you. So do you mean that DL work for language models only when they get a large amount of data?

"for example on tabular data where discontinuities are common, DL performs worse than alternatives, even if with more data it would eventually approximate a discontinuity." True. Is there references on this issue?

?

"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

Then why DL?

But why DL beats the curse? Why is DL the only class?

Yes a very finite grid size will approximate any digital image. But this is an approximation of an image in grids. How will it lead to approximation by NN?

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

Then why not use decision trees instead of DL?

Yes but is DL the unique mechanism? Why DL?

Then what will be the limitation of transformers?

Thank you. I understand the math. But I meant a real world example that "the solution is not in the model class."

>Large linear models tend not to scale well to large datasets if the solution is not in the model class

Will you provide me a reference?