Building a neural network with MS Excel in 2026 is no longer a gimmick; it is a practical tool for learning, prototyping, and solving simple classification problems. By combining the power of with the structured organization of spreadsheets, you can create robust AI models that are easy to understand and present.
A1[1]cap A sub 1 raised to the open bracket 1 close bracket power ): =1 / (1 + EXP(-Z_1^[1])) Repeat this logic for H2cap H sub 2 H3cap H sub 3
=MMULT(Hidden_Layer_Outputs, Output_Weights) + Output_Bias . build neural network with ms excel new
Wnew=Wold−(α⋅dW)cap W sub n e w end-sub equals cap W sub o l d end-sub minus open paren alpha center dot d cap W close paren (alpha) is the (e.g., 0.1 ).
=RANDARRAY(Inputs, HiddenNodes, -1, 1)
from sklearn.neural_network import MLPClassifier import xl data = xl.view("Table1") X = data[['Feature1', 'Feature2']] y = data['Target'] clf = MLPClassifier(hidden_layer_sizes=(4,), max_iter=500) clf.fit(X, y) Use code with caution. Automation via Office Scripts or VBA
Modern Excel provides powerful native functions that handle complex linear algebra seamlessly. You will rely heavily on these three core tools: Building a neural network with MS Excel in
Use the new Python-in-Excel capability to calculate gradients and update weights. Example PY() snippet: model.fit(X, y)
: This lets you create custom, reusable formulas. We will use it to build activation functions like ReLU and Sigmoid without writing VBA code. Wnew=Wold−(α⋅dW)cap W sub n e w end-sub equals
To learn, the network must calculate its errors and adjust its weights backward. Output Error ( δ2delta sub 2
In a dedicated section (e.g., columns A to C), initialize your weights with small random numbers between -0.5 and 0.5, and your biases to 0. Fill cells B3:C5 using the formula =RAND() - 0.5 . Biases 1 ( B1cap B sub 1 ): Enter 0 in cells B7:C7 . Weights 2 ( W2cap W sub 2 ): Fill cells B10:B11 using =RAND() - 0.5 . Biases 2 ( B2cap B sub 2 ): Enter 0 in cell B13 . 2. Prepare the Input and Target Data Set up a sample training row in row 16: Inputs (