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Polynomial regression and multilayer perceptrons have different structures and different learning procedures. What are these two algorithms pros and cons? Are there some situations where one should perform better than the other?

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  • $\begingroup$ If you have a neural network (aka a multilayer perceptron) with only an input and an output layer and with no activation function, that is exactly equal to linear regression. Quoting The Answer Below You Can Refer this Answer from Another Site of Stack Link $\endgroup$ – Aditya Mar 3 '18 at 20:21
  • $\begingroup$ This question seems overly broad. How about giving some more specifics of the scenario you're looking at? Different learning approaches exist because they work better for different scenarios. Maybe you wanna start with some basic book, course on machine learning before? $\endgroup$ – DallaRosa Apr 3 '18 at 3:35
  • $\begingroup$ Please summarize the main ideas here. Link-only answers are discouraged here. We don't want to be just a link farm; we want to build up useful new content of our own. $\endgroup$ – D.W. Apr 4 '18 at 5:22
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    $\begingroup$ I wrote about the drawbacks of polynomial regression here: madrury.github.io/jekyll/update/statistics/2017/08/04/… $\endgroup$ – Matthew Drury Apr 4 '18 at 16:26
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Polynomial regression can have multiple entries in the normal equation and it is not easy to say which polynomials you have to use in advance. Moreover, if you have lots of features you cannot handle memory errors most of the time. Nowadays people use MLPs and use batch normalization among layers for learning better. Those that you are referring to are a bit old algorithms but the former one is the logical mathematical solution for learning problems and the latter one is a beginning point for deep neural networks. I recommend taking a look at here and here.

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