Ms Excel Full [portable] — Build Neural Network With

Now we calculate exactly how much to alter each individual weight. We multiply the gradient by the input that fed into that weight. (Cell Y2): =$U2*N2 (Cell Z2): =$U2*O2 (Cell AA2): =$U2*P2 (Cell AB2): =$U2 Hidden Weight Gradients (Cells AC2:AH2): (Cell AC2): =$V2*A2 | (Cell AD2): =$V2*B2 | (Cell AE2): =$V2 (Cell AF2): =$W2*A2 | (Cell AG2): =$W2*B2 | (Cell AH2): =$W2 (Cell AI2): =$X2*A2 | (Cell AJ2): =$X2*B2 | (Cell AK2): =$X2 Drag all gradient formulas from row 2 down through row 5. 6. Training the Network (Optimization)

Toggling your reset switch will cause Excel to continuously cycle updates every time you hit the key. Conclusion

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At this stage, you have successfully built one single iteration (epoch) for a single row of data. To actually train the network, you have two choices in Excel: Method A: The Manual Copy-Paste (Iterative Steps) Copy your newly updated weights and biases. build neural network with ms excel full

We pass the output delta back through the second-layer weights to calculate the hidden neuron deltas. Column N11 Excel Formula: = M11 * $I$2 * E11 * (1 - E11) Hidden Delta 2 ( δh2delta sub h 2 end-sub ): Column O11 Excel Formula: = M11 * $I$3 * G11 * (1 - G11) Hidden Delta 3 ( δh3delta sub h 3 end-sub ): Column P11 Excel Formula: = M11 * $I$4 * I11 * (1 - I11) 6. Updating the Parameters

I'll produce an article titled: "How to Build a Neural Network in MS Excel (Full Guide) – No Coding Required". Structure:

Arthur opened a blank workbook. He wasn't just typing numbers; he was architecting a digital brain. Now we calculate exactly how much to alter

) represents the "activation" or the final prediction of your neuron.

| Cell | Content | |------|----------------------------------------------| | A2 | 0 (x1 training example 1) | | B2 | 0 (x2 training example 1) | | C2 | 0 (target) | | E1 | 0.3 (w11) | | E2 | 0.1 (w12) | | F1 | -0.2 (w21) | | F2 | 0.4 (w22) | | G1 | 0 (b1) | | G2 | 0 (b2) | | D2 | =$E$1 A2 + $E$2 B2 + $G$1 | | E2 | =1/(1+EXP(-D2)) | | ... | ... |

In Excel syntax: = (Prediction_Cell - Target_Cell) * Prediction_Cell * (1 - Prediction_Cell) 2. Hidden Layer Error This link or copies made by others cannot be deleted

After Excel runs its iterations (press F9 repeatedly if not using iterative mode), test your XOR network.

Final note This Excel implementation teaches core NN math by making every intermediate derivative explicit. For reproducibility, keep copies of initial random seeds (or fixed initial weights) and record the epoch log. For production or larger experiments, migrate the same formulas to code (Python) for efficiency and flexibility.

using NumPy to see how formulas map to real code. Share public link

Arthur arranged his worksheet.

[Input Layer] [Hidden Layer] [Output Layer] ( X1 ) ----------> ( H1 ) \ \ / ( H2 ) ---> ( Y-hat ) ---> [Loss Calculation] \ / / ( X2 ) ----------> ( H3 ) / The Sample Dataset