I am learning a bit about machine learning, and I frequently see things labeled "linear function" or "nonlinear function."
For example when discussing a neural network created by combining individual logistic regression nodes, a book I read noted that logistic regression is a linear function, and that the entire network would be no more powerful than a linear model except that a nonlinear function (such as relu or tanh) is applied to each node. I don't really appreciate this, because I don't have a firm grasp on the definition of linear function.
To use a simpler (to me) example, I was confused when I learned that both SVM and linear regression are linear models. It is easy for me to see how linear regression is a linear model, because its calculations are similar to the definition of a line,
y = mx + b, that I learned in middle school. SVM seems more exotic to me.
So how can we know if something is a linear function/model versus a nonlinear one? Does it have to do with how the inputs are gathered, or the nature of the calculations afterward?