- #1

jonjacson

- 453

- 38

- TL;DR Summary
- I don't understand what are the advantages of backpropagation in a neural network versus using classical interpolation/extrapolation methods.

Hi guys,

I was learning machine learning and I found something a bit confusing.

When I studied physics I saw the method of least squares to find the best parameters for the given data, in this case we assume we know the equation and we just minimize the error. So if it is a straight line model we compute the best slope and constant.

With machine learning we don't know the underlying equation or model so we use a general method to "fit" as best as we can the real data.

But, isn't that what we do with interpolation/extrapolation?

What is it so special about a neural network and the backpropagation method that we can't achieve with interpolation?

I was learning machine learning and I found something a bit confusing.

When I studied physics I saw the method of least squares to find the best parameters for the given data, in this case we assume we know the equation and we just minimize the error. So if it is a straight line model we compute the best slope and constant.

With machine learning we don't know the underlying equation or model so we use a general method to "fit" as best as we can the real data.

But, isn't that what we do with interpolation/extrapolation?

What is it so special about a neural network and the backpropagation method that we can't achieve with interpolation?