Build Neural Network With Ms Excel New __top__ -

delta_hidden = MMULT(delta_output, TRANSPOSE(W2)) * HiddenActivation * (1 - HiddenActivation)

Let’s put these into an Excel worksheet. Name one sheet . In rows 1‑7, store:

Building a neural network in Microsoft Excel might sound counterintuitive in an era dominated by powerful deep‑learning frameworks like TensorFlow and PyTorch. But setting up a small neural network in a spreadsheet is one of the most powerful ways to truly understand how these models work under the hood. Every calculation stays visible, every intermediate value can be inspected, and the “black box” of machine learning becomes completely transparent. This guide walks you through everything you need to know—from the fundamental concepts to the latest tools and trends emerging in 2025 and 2026.

If you do not want to set up iterative loops manually, Excel's built-in can automate gradient descent for you. Go to Data > Solver . Set the Objective to your Mean Loss cell (minimize it).

Modern Excel allows you to build custom, reusable functions without VBA using LAMBDA . We need two activation functions: ReLU and Sigmoid. build neural network with ms excel new

Create an containing your training data features (e.g., Columns A and B).

): Subtract your target values from your final predictions ( Hidden Layer Error ( δ1delta sub 1

At this point you have a working neural network that takes x and produces a prediction. Try changing a weight in the Parameters sheet—you’ll see the entire curve update instantly. That immediate feedback is what makes Excel so powerful for learning.

Excel will perform three specific operations: But setting up a small neural network in

Use a to compare the predicted outputs ( Predicted ) against the actual inputs ( Actual ). If the neural network is trained correctly, the scatter plot should show a clear linear relationship, indicating the network has learned the underlying pattern. Conclusion

A brain doesn't just pass on every signal; it "fires" only when a signal is strong enough. We use a to squash our sum into a number between 0 and 1. Formula: =1/(1+EXP(-SumCell))

In a new table, use: =MMULT(Normalized_Inputs, Hidden_Weights) + Hidden_Biases .

weight matrix, enter this formula in cell B2 of your weights section: =RANDARRAY(2, 3, -0.5, 0.5) Use code with caution. If you do not want to set up

To keep the spreadsheet clean and understandable, we will build a network with the following architecture: Accepts two numerical features (

=RANDARRAY(1, 1, -0.5, 0.5)

In cell S1 , insert your trained output: =1/(1+EXP(-(MMULT(IF(MMULT(Q1:R1, W1)+B1>0, MMULT(Q1:R1, W1)+B1, 0), W2) + B2)))

Create a "Epoch counter" in cell A1 . In A2 , type: =IF(A1=1000, 0, A1+1)