As I understand it, in a deep neural network, we use an activation function (g) after applying the weights (w) and bias(b) (z := w * X + b | a := g(z)). So there is a composition function of (g o z) and the activation function makes so our model can learn function other than linear functions. I see that Sigmoid and Tanh activation function makes our model non-linear, but I have some trouble seeing that a ReLu (which takes the max out of 0 and z) can make a model non-linear...
Let's say if every Z is always positive, then it would be as if there was no activation function...
So why does ReLu make a neural network model non-linear?
Deciding if a function is linear or not is of course not a matter of opinion or debate; there is a very simple definition of a linear function, which is roughly:
f(a*x + b*y) = a*f(x) + b*f(y)
for every x & y in the function domain and a & b constants.
The requirement "for every" means that, if we are able to find even a single example where the above condition does not hold, then the function is nonlinear.
Assuming for simplicity that a = b = 1, let's try x=-5, y=1 with f being the ReLU function:
f(-5 + 1) = f(-4) = 0
f(-5) + f(1) = 0 + 1 = 1
so, for these x & y (in fact for every x & y with x*y < 0) the condition f(x + y) = f(x) + f(y) does not hold, hence the function is nonlinear...
The fact that we may be able to find subdomains (e.g. both x and y being either negative or positive here) where the linearity condition holds is what defines some functions (such as ReLU) as piecewise-linear, which are still nonlinear nevertheless.
Now, to be fair to your question, if in a particular application the inputs happened to be always either all positive or all negative, then yes, in this case the ReLU would in practice end up behaving like a linear function. But for neural networks this is not the case, hence we can rely on it indeed to provide our necessary non-linearity...