1
$\begingroup$

In multi-Linear regression where we have a model of $y=X_1+X_2$ it's a common practice (as I studied in my Master's) to increase the dimensionality and try to use a model of $y=X_1+X_2+X_1 X_2$. Can we say the same about increasing the nodes and the hidden layers in neural networks?

I'm a beginner and English is not my first language so sorry If I made a mistake or if it's a stupid question.

$\endgroup$

1 Answer 1

0
$\begingroup$

Any function $f(X)$ can be represented by a neural network of sufficient complexity, and as the size of the network is increased (number of nodes and/or number of layers), it can represent more complex functions. While you can think of this as similar to adding interaction terms in a linear model, neural networks are not limited to linear functions due to the use of non-linear activation functions. The other difference is that we don't control what terms or functions get included in the model as they are learnt during the training process.

$\endgroup$
1
  • $\begingroup$ Hi, what you mean by "represent more complex functions"? $\endgroup$
    – mrcoet
    Nov 25, 2022 at 12:06

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.