With Ms Excel !!install!! Full: Build Neural Network

Excel will iterate through thousands of weight combinations until the Loss Function is minimized. Once it stops, you have a trained model. You can change the input values (

Finally, he built the . He created a "New Weights" section. The formula was: Old Weight + (Learning Rate * Delta * Input) .

Building a neural network in Microsoft Excel is an excellent way to demystify "black box" AI by manually implementing and backpropagation using standard cell formulas. To build a simple 2-input, 1-output network, you must calculate the weighted sum of inputs, apply an activation function, and then use the Excel Solver or manual calculus to minimize error. 1. Structure Your Spreadsheet

In row 7, compute the average gradient for all parameters across rows 2 through 5. For example: Cell W7 (Average

In cell AB2 (Error for Hidden Neuron 2), enter: =($Y2 * $I$3) * P2 * (1 - P2) build neural network with ms excel full

For each row of data, we need to calculate the predicted output. We will build these calculations sequentially from column K to column S. Step 1: Calculate Hidden Layer Dot Products ( Z(1)cap Z raised to the open paren 1 close paren power The net input to a hidden node is calculated as: In row 2, enter these formulas: =(A2*$H$2)+(B2*$H$3)+$H$4 Cell L2 ( Z2cap Z sub 2 ): =(A2*$I$2)+(B2*$I$3)+$I$4 Cell M2 ( Z3cap Z sub 3 ): =(A2*$J$2)+(B2*$J$3)+$J$4 Step 2: Apply the Sigmoid Activation Function ( A(1)cap A raised to the open paren 1 close paren power The Sigmoid formula is: . In Excel, this is represented using EXP() . Cell N2 ( H1cap H sub 1 Output): =1/(1+EXP(-K2)) Cell O2 ( H2cap H sub 2 Output): =1/(1+EXP(-L2)) Cell P2 ( H3cap H sub 3 Output): =1/(1+EXP(-M2)) Step 3: Calculate Output Layer Dot Product ( Z(2)cap Z raised to the open paren 2 close paren power

For this guide, we will build a simple feedforward network consisting of: : Two features (

This is where the network makes a prediction. We'll assume a single hidden layer with 2 neurons and a Sigmoid activation function.

Calculate the weighted sum for the output neuron using the hidden activations. Z(2)cap Z raised to the open paren 2 close paren power (Cell P2): =SUMPRODUCT(M2:O2, $F$7:$H$7) + $I$7 Step 4: Output Layer Activation / Prediction ( A(2)cap A raised to the open paren 2 close paren power Ypredcap Y sub p r e d end-sub Calculate the final predicted output. Ypredcap Y sub p r e d end-sub (Cell Q2): =1 / (1 + EXP(-P2)) Step 5: Error Calculation (Loss) Excel will iterate through thousands of weight combinations

You have successfully built, trained, and validated a neural network using nothing but MS Excel formulas. You visually tracked the loss curve, manually implemented forward propagation, derived the gradients in back propagation, and updated weights using gradient descent.

To keep the spreadsheet manageable while demonstrating deep learning, we will build a . Input Layer (Layer 0): 2 neurons ( ) representing our data features. Hidden Layer (Layer 1): 3 neurons ( ) to capture non-linear relationships. Output Layer (Layer 2): 1 neuron ( Ypredcap Y sub p r e d end-sub ) to output the final prediction.

The gradient for any weight is equal to the input coming into that weight multiplied by the error gradient ( ) of the receiving neuron.

He hit .

Sigmoid for all hidden and output nodes Loss Function: Mean Squared Error (MSE) 2. Setting Up the Excel Workspace

A basic neural network (like one for the XOR problem or simple classification) typically needs three layers: Your raw data (e.g., X1cap X sub 1 X2cap X sub 2

| A | B | C | | :--- | :--- | :--- | | | X2 | Y (Target) | | 0 | 0 | 0 | | 0 | 1 | 1 | | 1 | 0 | 1 | | 1 | 1 | 0 |