Confusion with Notation in the Book on Deep Learning by Ian Goodfellow et al

In chapter 6.1 on ‘Example: Learning XOR‘, the bottom of page 168 mentions:

The activation function $g$ is typically chosen to be a function that
is applied element-wise, with $h_i = g(x^TW_{:,i}+c_i).$

Then we see equation 6.3 is defined as (assuming g as ReLU):

We can now specify our complete network as
$f(x; W,c,w,b) = w^T$
max${0, W^Tx + c} + b$

Wondering why the book uses $W^Tx$ in equation 6.3, while I expect it to be $x^TW$. Unlike XOR example in the book where $W$ is a $2times2$ square matrix, we may have non-square $W$ as well, and in such cases, $x^TW$ is not same as $W^Tx$.

Please help me understand, if I’m missing something here.