Activation functions are used to introduce non-linearities in the linear output of the type w * x + b in a neural network.

Which I am able to understand intuitively for the activation functions like sigmoid.

I understand the advantages of ReLU, which is avoiding dead neurons during backpropagation. However, I am not able to understand why is ReLU used as an activation function if its output is linear?

Doesn't the whole point of being the activation function get defeated if it won't introduce non-linearity?

In mathematics (linear algebra) a function is considered linear whenever a function$f: A  \rightarrow B$ if for every $x$ and $y$ in the domain $A$ has the following property:  $f(x) + f(y) = f(x+y)$.  By definition the ReLU is $max(0,x)$.  Therefore, if we split the domain from $(-\infty, 0]$ or $[0, \infty)$ then the function is linear.  However, it's easy to see that $f(-1) + f(1) \neq f(0)$.  Hence by definition ReLU is not linear.
Nevertheless, ReLU is so close to linear that this often confuses people and wonder how can it be used as a universal approximator.  In my experience, the best way to think about them is like Riemann sums.  You can approximate any continuous functions with lots of little rectangles.  ReLU activations can produced lots of little rectangles.  In fact, in practice, ReLU can make rather complicated shapes and approximate many complicated domains.
I also feel like clarifying another point.  As pointed out by a previous answer, neurons do not die in Sigmoid, but rather vanish.  The reason for this is because at maximum the derivative of the sigmoid function is .25.  Hence, after so many layers you end up multiplying these gradients and the product of very small numbers less than 1 tend to  go to zero very quickly.
Hence if you're building a deep learning network with a lot of layers, your sigmoid functions will essentially stagnant rather quickly and become more or less useless.
The key take away is the vanishing comes from multiplying the gradients not the gradients themselves.
