In some sense, you're right that a neural net is just another tool to fit data. However, it's quite the tool! There's this universal approximation theorem saying that, under decent conditions, a neural network can get as close as you want to a wide class of functions. This means that you can get the network to give you complicated shapes with squiggles all over if that's the right trend.

The universal approximation theorem is a big upside. You don't have to specify that you want to model with sine curves or a particular type of spline. You just let the computer figure that out for you. The result is the ability to model complex patterns and make accurate predictions. The drawback is that the modeling can pick up on coincidences in the data that look like a trend but are not. This causes overfitting. When your goal is to make accurate predictions, a model that has overfit does nothing for you. A second drawback is that neural networks are hard to interpret. A third drawback is that they can take a long time to train, while a linear regression is just a matrix inversion and a couple of matrix products (the $\hat{\beta}=(X^TX)^{-1}X^Ty$).

In some sense, you're right that a neural net is just another tool to fit data. However, it's quite the tool! There's this universal approximation theorem saying that, under decent conditions, a neural network can get as close as you want to a wide class of functions. This means that you can get the network to give you complicated shapes with squiggles all over if that's the right trend.

The universal approximation theorem is a big upside. You don't have to specify that you want to model with sine curves or a particular type of spline. You just let the computer figure that out for you. The result is the ability to model complex patterns and make accurate predictions. The drawback is that the modeling can pick up on coincidences in the data that look like a trend but are not. This causes overfitting. When your goal is to make accurate predictions, a model that has badly overfit does nothing for you. A second drawback is that neural networks are hard to interpret. A third drawback is that they can take a long time to train, while a linear regression is just a matrix inversion and a few matrix products (the $\hat{\beta}=(X^TX)^{-1}X^Ty$).

In some sense, you're right that a neural net is just another tool to fit data. However, it's quite the tool! There's this universal approximation theorem saying that, under decent conditions, a neural network can get as close as you want to a wide class of functions. This means that you can get the network to give you complicated shapes with squiggles all over if that's the right trend.

The universal approximation theorem is a big upside. You don't have to specify that you want to model with sine curves or a particular part of spline. You just let the computer figure that out for you. The result is the ability to model complex patterns and make accurate predictions. The drawback is that the modeling can pick up on coincidences in the data that look like a trend but are not. This causes overfitting. When your goal is to make accurate predictions, a model that has overfit does nothing for you. A second drawback is that neural networks are hard to interpret. A third drawback is that they can take a long time to train, while a linear regression is just a matrix inversion and a couple of matrix products (the $\hat{\beta}=(X^TX)^{-1}X^Ty$).

In some sense, you're right that a neural net is just another tool to fit data. However, it's quite the tool! There's this universal approximation theorem saying that, under decent conditions, a neural network can get as close as you want to a wide class of functions. This means that you can get the network to give you complicated shapes with squiggles all over if that's the right trend.

The universal approximation theorem is a big upside. You don't have to specify that you want to model with sine curves or a particular type of spline. You just let the computer figure that out for you. The result is the ability to model complex patterns and make accurate predictions. The drawback is that the modeling can pick up on coincidences in the data that look like a trend but are not. This causes overfitting. When your goal is to make accurate predictions, a model that has overfit does nothing for you. A second drawback is that neural networks are hard to interpret. A third drawback is that they can take a long time to train, while a linear regression is just a matrix inversion and a couple of matrix products (the $\hat{\beta}=(X^TX)^{-1}X^Ty$).