One of the most fundamental concepts of neural networks is an operation known as an operational gate. In this section, we will start with a multiplication operation as a gate and then we will consider nested gate operations.

The first operational gate we will implement looks like *f(x)=a.x*. To optimize this gate, we declare the *a* input as a variable and the *x* input as a placeholder. This means that TensorFlow will try to change the *a* value and not the *x* value. We will create the loss function as the difference between the output and the target value, which is 50.

The second, nested operational gate will be *f(x)=a.x+b*. Again, we will declare *a* and *b* as variables and *x* as a placeholder. We optimize the output ...

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