Build Neural Network With Ms Excel Full ((full)) May 2026

To build a functional neural network in Microsoft Excel, you must manually implement the mathematics of forward propagation and use tools like Excel Solver for optimization or complex formulas for backpropagation. This approach is an excellent way to visualize the "black box" of AI through a transparent spreadsheet environment. 1. Set Up the Network Architecture

Start with a simple structure, such as a Multi-Layer Perceptron (MLP) for classification or regression.

Input Layer: Define cells for your independent variables (e.g.,

Hidden Layer: Create a layer with two or more neurons to handle non-linear patterns.

Output Layer: A single cell for the final prediction (e.g., predicted probability or value).

Weights and Biases: Dedicate a separate range of cells for these parameters. Initialize them with small random values to start the learning process. 2. Implement Forward Propagation

Forward propagation involves calculating the network's output based on current weights.

To build a full neural network in Microsoft Excel use standard formulas to handle the core mechanics: Forward Propagation (making predictions) and Backpropagation (learning from errors) Towards Data Science 1. Structure Your Spreadsheet build neural network with ms excel full

First, organize your workbook into layers. A basic network consists of an Input Layer , at least one Hidden Layer Output Layer Towards Data Science Weights and Biases : Create dedicated tables for weights ( ) and biases ( ). Use the function to initialize these with small random values. Data Table : Input your training data (features like ) and the target values ( www.mynextemployee.com 2. Forward Propagation (The Prediction)

This step calculates the network's output by passing inputs through each layer. Towards Data Science Weighted Sum

: For each neuron, calculate the sum of inputs multiplied by their weights plus the bias. Use the SUMPRODUCT function or matrix multiplication: =SUMPRODUCT(Inputs, Weights) + Bias Activation Function

: Pass the weighted sum through a non-linear function like the to get the neuron's final output. =1 / (1 + EXP(-WeightedSum)) www.mynextemployee.com 3. Backpropagation (The Learning)

To "teach" the network, you must update the weights based on the error. Towards AI Error Calculation

: Measure the difference between the predicted output and the actual target. Gradient Descent

: Calculate the derivative of the error with respect to each weight. In Excel, this involves several columns of formulas to "backpropagate" the error from the output layer to the hidden layer. Update Weights : Adjust the original weights using a Learning Rate (typically a small value like 0.01). New Weight = Old Weight - (Learning Rate * Gradient) www.mynextemployee.com 4. Training and Optimization To build a functional neural network in Microsoft

: Drag your formulas down for hundreds or thousands of rows to simulate multiple "epochs" of training. Excel Solver : For a more automated approach, you can use the built-in Solver Add-in to minimize the error by changing the weights and biases. www.mynextemployee.com Resources for Advanced Builds Neural Network Regressor in Excel - Towards Data Science

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Gradients for Hidden Layer (Row 20)

• Delta Hidden 1: (Delta_Output * W11_O) * H1_Act * (1 - H1_Act)
  • Cell P20: =(L20 * B18) * (G20 * (1 - G20))
• Delta Hidden 2: (Delta_Output * W12_O) * H2_Act * (1 - H2_Act)
  • Cell Q20: =(L20 * C18) * (H20 * (1 - H20))
• Gradients for W11, W12, W21, W22: Multiply deltas by corresponding inputs (X1 or X2).
  • Cell R20 (Grad W11): =P20 * B20 (X1)
  • Cell S20 (Grad W12): =P20 * C20 (X2)
  • Cell T20 (Grad W21): =Q20 * B20
  • Cell U20 (Grad W22): =Q20 * C20
• Gradients for Hidden Biases:
  • Cell V20: =P20
  • Cell W20: =Q20

Copy rows L20:W20 down for rows 21, 22, 23.

Step 8: Training Loop (The Manual Step)

Because Excel doesn't have a native loop without VBA, you have two options:

Option A: Copy-Paste Iteration (Simple for learning)

1. Copy the "Updated Weights" cells.
2. Select your original "Initial Weights" cells.
3. Right-click -> Paste Special -> Values.
4. Watch the Total Loss (Cell B5) decrease.
5. Repeat 500 times (or until loss < 0.005).

Option B: Circular References (Advanced/Automatic)

1. Go to File -> Options -> Formulas -> Enable Iterative Calculations (Max Iterations: 5000).
2. In the "Initial Weights" cell, write: =IF(ISBLANK(Control_Cell), RAND(), New_Weight_Cell).
3. Toggle the Control_Cell to trigger training. (Complex but powerful).

Hidden Layer Calculations

• Hidden Weighted Sum (Z1)
  In E14:
  =SUMPRODUCT($B$4:$E$5, B14:C14) – wait, this is tricky. Let's do clean formulas: Gradients for Hidden Layer (Row 20)

  Better approach:
  For h1 weighted sum (cell F14):
  =B14*$B$4 + C14*$C$4 + $G$4
  (x1 * w11 + x2 * w21 + bias1)

  For h2 (cell G14):
  =B14*$D$4 + C14*$E$4 + $G$5

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

  For h4 (cell I14):
  =B14*$D$5 + C14*$E$5 + $G$7

• Hidden Activation (ReLU or Sigmoid) – Use Sigmoid for smoother gradients.
  Sigmoid = =1/(1+EXP(-F14)). Put in J14, K14, L14, M14.

Step 3.2: The Hidden Layer Activation (A_hidden)

Sigmoid function: $\frac11+e^-z$

• Cell G2: =1/(1+EXP(-B2)) (Targets H1 Z value)
• Cell H2: =1/(1+EXP(-C2))
• Cell I2: =1/(1+EXP(-D2))
• Cell J2: =1/(1+EXP(-E2))
• Drag rows G2:J2 down to row 5.

Part 3: Forward Propagation (The Prediction Engine)

Go to the Forward_Prop tab.

Step 3.3: The Output Layer (Z_out & Prediction)

• Cell M2 (Z_out): =SUMPRODUCT(G2:J2, Parameters!$G$2:$J$2) + Parameters!$L$2
• Cell N2 (Prediction ŷ): =1/(1+EXP(-M2))
• Drag M2:N2 down to row 5.