Corrected definition of intermediate gradient value

optimization-2.md

@@ -202,7 +202,7 @@ invden = 1.0 / den                                       #(7)
 f = num * invden # done!                                 #(8)
 ```
 
-Phew, by the end of the expression we have computed the forward pass. Notice that we have structured the code in such way that it contains multiple intermediate variables, each of which are only simple expressions for which we already know the local gradients. Therefore, computing the backprop pass is easy: We'll go backwards and for every variable along the way in the forward pass (`sigy, num, sigx, xpy, xpysqr, den, invden`) we will have the same variable, but one that begins with a `d`, which will hold the gradient of that variable with respect to the output of the circuit. Additionally, note that every single piece in our backprop will involve computing the local gradient of that expression, and chaining it with the gradient on that expression with a multiplication. For each row, we also highlight which part of the forward pass it refers to:
+Phew, by the end of the expression we have computed the forward pass. Notice that we have structured the code in such way that it contains multiple intermediate variables, each of which are only simple expressions for which we already know the local gradients. Therefore, computing the backprop pass is easy: We'll go backwards and for every variable along the way in the forward pass (`sigy, num, sigx, xpy, xpysqr, den, invden`) we will have the same variable, but one that begins with a `d`, which will hold the gradient of the output of the circuit with respect to that variable. Additionally, note that every single piece in our backprop will involve computing the local gradient of that expression, and chaining it with the gradient on that expression with a multiplication. For each row, we also highlight which part of the forward pass it refers to:
 
 ```python
 # backprop f = num * invden