Thats a bit far. Relu does check x>0 but thats just one non-linearity in the linear/non-linear sandwich that makes up universal function approximator theorem. Its more conplex than just x>0

The relu/if-then-else is in fact centrally important as it enables computations with complex control flow (or more exactly, conditional signal flow or gating) schemes (particularly as you add more layers).

Multiply-accumulate, then clamp negative values to zero. Every even-numbered variable is a weighted sum plus a bias (an affine transformation), and every odd-numbered variable is the ReLU gate (max(0, x)). Layer 2 feeds on the ReLU outputs of layer 1, and the final output is a plain linear combination of the last ReLU outputs

    // inputs: u, v
    // --- hidden layer 1 (3 neurons) ---
    let v0  = 0.616*u + 0.291*v - 0.135
    let v1  = if 0 > v0 then 0 else v0
    let v2  = -0.482*u + 0.735*v + 0.044
    let v3  = if 0 > v2 then 0 else v2
    let v4  = 0.261*u - 0.553*v + 0.310
    let v5  = if 0 > v4 then 0 else v4
    // --- hidden layer 2 (2 neurons) ---
    let v6  = 0.410*v1 - 0.378*v3 + 0.528*v5 + 0.091
    let v7  = if 0 > v6 then 0 else v6
    let v8  = -0.194*v1 + 0.617*v3 - 0.291*v5 - 0.058
    let v9  = if 0 > v8 then 0 else v8
    // --- output layer (binary classification) ---
    let v10 = 0.739*v7 - 0.415*v9 + 0.022
    // sigmoid squashing v10 into the range (0, 1)
    let out = 1 / (1 + exp(-v10))