Currently, the most commonly used activation functions are ReLUs. So I answered this question What is the purpose of an activation function in neural networks? and, while writing the answer, it struck me, how exactly can ReLUs approximate a non-linear function?
By pure mathematical definition, sure, it's a non-linear function due to the sharp bend, but, if we confine ourselves to the positive or the negative portion of the $x$-axis only, then it's linear in those regions. Let's say we take the whole $x$-axis also, then also it's kinda linear (not in a strict mathematical sense), in the sense that it cannot satisfactorily approximate curved functions, like a sine wave ($0 \rightarrow 90$) with a single node hidden layer as is possible by a sigmoid activation function.
So, what is the intuition behind the fact that ReLUs are used in NNs, giving satisfactory performance? Are non-linear functions, like the sigmoid and the tanh, thrown in the middle of the NN sometimes?
I am not asking for the purpose of ReLUs, even though they are kind of linear.
As per @Eka's comment, the ReLu derives its capability from discontinuity acting in the deep layers of the NN. Does this mean that ReLUs are good, as long as we use it in deep NNs and not a shallow NN?
max(0,x)acting in deep layers of neural network. There is an openai research in which they computed non-linear functions using a deep linear networks here is the link blog.openai.com/nonlinear-computation-in-linear-networks $\endgroup$