| | F | G | | --- | --- | --- | | 1 | Activation Functions | | | 2 | Sigmoid | =1/(1+EXP(-A2)) | | 3 | ReLU | =MAX(0,A3) | | 4 | Tanh | =2/(1+EXP(-2*A4))-1 |

: Designate a cell for each parameter. For this model, you will need: 4 weights ( ) for the input-to-hidden layer. 2 biases ( ) for the hidden neurons. 2 weights ( ) and 1 bias ( boutb sub o u t end-sub ) for the output neuron.

For h3 (cell H14 ): =B14*$B$5 + C14*$C$5 + $G$6

Multiply the Output Error Gradient by the Hidden Layer Activations. Hidden Layer Error: