The update rule is: New Weight = Old Weight - (Learning Rate * Average Gradient)
Create an "Updated Weights" section next to your initial weights.
Repeat for all weights and biases.
Arthur arranged his worksheet.
He took a deep breath. He entered the four scenarios of XOR into rows 2 through 5:
He clicked the LEARN button.
The screen flickered. The VBA script ran, copying and pasting values rapidly. The line chart on the right, representing "Total Error," plummeted. It was a jagged descent, a jagged heartbeat of a digital creature learning to think.
The Output cell (K2) began to shift.
Arthur watched the row for (0,1). The target was 1. The Output cell climbed. 0.6... 0.8... 0.92... 0.99.
The script stopped after 1,000 iterations.
Arthur looked at the results.
When you hear "Neural Network," you typically think of Python, TensorFlow, or PyTorch. But beneath all those high-level libraries lies pure mathematics: linear algebra, calculus, and iterative optimization. Microsoft Excel, despite being a spreadsheet tool, is surprisingly capable of executing these operations manually.
Why build a neural network in Excel?
The caveat: Excel is slow. Do not attempt this for production or ImageNet. We are building a single hidden layer network for a binary classification problem (XOR logic gate).
We will build a 3-layer network:
The Math (Forward Propagation): $$Z_hidden = X \cdot W_input\rightarrow hidden + b_hidden$$ $$A_hidden = \sigma(Z_hidden)$$ $$Z_output = A_hidden \cdot W_hidden\rightarrow output + b_output$$ $$A_output = \sigma(Z_output)$$
$$\textLoss = -[y \log(\haty) + (1-y) \log(1-\haty)]$$
This is the hardest part to explain, but Excel makes it mechanical. We need derivatives.
Derivative of Sigmoid: $\fracd\sigmadz = \sigma(z) \cdot (1 - \sigma(z))$
Sigmoid function: $\frac11+e^-z$
Introduction
A simple neural network can be implemented entirely in Excel to illustrate how forward propagation, backpropagation, and weight updates work. This guide builds a compact feedforward network (one hidden layer) for a binary classification or regression task using only Excel formulas and iterative recalculation. No VBA required.
What you'll get
Assumptions (reasonable defaults)
Sheet layout (recommended)
Step-by-step: set up cells and formulas
Implement updates: two approaches
Training loop example (manual)
Stopping criteria
Debugging tips
Scaling & limitations
Optional: Implementing cross-entropy (brief)
Visualization
Complete example workbook structure (quick map)
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.
If you want, I can:
Which would you like?
Building a neural network in Microsoft Excel is an excellent way to visualize the "black box" of machine learning. By using standard formulas and the Excel Solver add-in, you can create a functional model without writing complex code. Architecture Overview
For this guide, we will build a simple feedforward network consisting of: Input Layer: Two features (
Hidden Layer: Two neurons with a Sigmoid activation function. Output Layer: One neuron for classification or regression. Step 1: Set Up Your Data and Parameters Organize your spreadsheet into three main sections: Training Data: Create columns for your inputs ( ) and the known target output (
Weights and Biases: Designate a cell for each parameter. For this model, you will need: 4 weights ( ) for the input-to-hidden layer. 2 biases ( ) for the hidden neurons. 2 weights ( ) and 1 bias ( boutb sub o u t end-sub ) for the output neuron.
Initialization: Use =RAND() to assign small, random initial values to all weights and biases. Step 2: Implement Forward Propagation
In new columns next to your training data, calculate the flow of information through the network using standard formulas: Training a Neural Network in a Spreadsheet
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
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
: 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
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.
Building a neural network in Microsoft Excel is an excellent way to demystify "black box" AI by manually implementing forward propagation 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
Set up a "Forward Pass" area where data flows from inputs to the final prediction. Inputs ( ): Reserve cells for your input features (e.g., Weights ( ) and Bias (
): Create a section for trainable parameters. Initialize these with small random numbers (e.g., between -1 and 1). Weighted Sum (
): Use the SUMPRODUCT formula to multiply inputs by their respective weights and add the bias:=SUMPRODUCT(Input_Range, Weight_Range) + Bias_Cell 2. Apply the Activation Function
Neural networks require a non-linear activation function to learn complex patterns. The Sigmoid function is the most common for Excel-based models because its formula is straightforward. Formula: Excel Implementation: =1 / (1 + EXP(-z_cell)) This output (
) represents the "activation" or the final prediction of your neuron. 3. Calculate the Error (Loss Function)
To "teach" the network, you must measure how far off its prediction is from the actual target ( ). Use Mean Squared Error (MSE) for this calculation. Error Formula: Excel Implementation: =(Prediction_Cell - Actual_Cell)^2 4. Train the Network (Backpropagation)
The "learning" happens when you adjust weights to reduce the error. You have two main options in Excel:
Create a PivotTable to analyze worksheet data - Microsoft Support
Building a neural network in Excel is possible using native formulas like SUMPRODUCT
function for forward propagation, and manual calculus for backpropagation. Towards Data Science 1. Structure the Architecture build neural network with ms excel full
Set up your spreadsheet with distinct sections for inputs, weights, hidden layers, and outputs. Towards Data Science Input Layer : Assign cells for your features (e.g., Weights and Biases : Initialize a separate table with random values using Hidden Layer
: Create neurons that will process the weighted sum of inputs. Towards Data Science 2. Implement Forward Propagation
This step calculates the network's prediction by moving data from inputs to outputs. Towards Data Science Weighted Sum
: For each neuron, calculate the sum of inputs multiplied by their weights plus a bias. Excel Formula: =SUMPRODUCT(InputsRange, WeightsRange) + BiasCell Activation Function : Apply the
function to the weighted sum to introduce non-linearity, which keeps outputs between 0 and 1. Excel Formula: =1 / (1 + EXP(-SumCell)) Towards Data Science 3. Calculate Error (Loss)
Determine how far the network's prediction is from the actual target value. Towards Data Science Mean Squared Error (MSE) for regression tasks. Excel Formula: =(Actual - Predicted)^2 Towards Data Science 4. Backpropagation (Training)
To train the model without macros, you must manually calculate the partial derivatives (gradients) for each weight and bias using the chain rule. Training a Neural Network in a Spreadsheet
Building a neural network from scratch in Microsoft Excel is one of the most effective ways to demystify "Black Box" AI. By stripping away complex libraries like TensorFlow, you can see the raw mathematics of forward and backward propagation in action across a grid.
This guide provides a full walkthrough for building a multi-layer perceptron (MLP) to solve a simple non-linear problem, such as the XOR gate. 1. Structure Your Spreadsheet A basic neural network typically consists of three layers: Input Layer: Two nodes (
Hidden Layer: At least two neurons to handle non-linear relationships. Output Layer: One neuron ( yhaty sub h a t end-sub ) for the final prediction.
Initialization Step:Start by assigning random weights (between -1 and 1) to every connection between layers. You can use Excel's =RAND() or =RANDBETWEEN(-1, 1) functions. 2. Implement Forward Propagation
Forward propagation is the process of turning inputs into a prediction using the current weights. Neural Network Regressor in Excel - Towards Data Science
Build a Neural Network with MS Excel: A Step-by-Step Guide
Microsoft Excel is a widely used spreadsheet software that is often associated with financial analysis, budgeting, and data management. However, its capabilities extend far beyond these areas, and it can be used to build a neural network from scratch. In this article, we will explore how to build a neural network with MS Excel, without any prior programming knowledge.
What is a Neural Network?
A neural network is a machine learning model inspired by the structure and function of the human brain. It consists of layers of interconnected nodes or "neurons," which process inputs and produce outputs. Neural networks are capable of learning complex patterns in data and making predictions or classifications.
Why Build a Neural Network with MS Excel?
Building a neural network with MS Excel may seem unconventional, but it has several advantages:
Neural Network Components
Before building a neural network with MS Excel, let's review the basic components:
Setting Up the Neural Network in MS Excel
To build a neural network with MS Excel, we will use the following steps:
Step 1: Prepare the Data
Suppose we want to build a neural network that predicts the output of a simple XOR (exclusive OR) function. The XOR function takes two binary inputs and produces an output of 1 if the inputs are different and 0 if they are the same.
| Input 1 | Input 2 | Output | | --- | --- | --- | | 0 | 0 | 0 | | 0 | 1 | 1 | | 1 | 0 | 1 | | 1 | 1 | 0 |
Step 2: Create a Neural Network Architecture
For this example, we will create a simple neural network with:
Step 3: Initialize Weights and Biases
Create a table to store the weights and biases for each connection:
| Connection | Weight | Bias | | --- | --- | --- | | Input 1 -> Hidden 1 | 0.5 | 0.2 | | Input 1 -> Hidden 2 | 0.3 | 0.1 | | Input 2 -> Hidden 1 | 0.2 | 0.4 | | Input 2 -> Hidden 2 | 0.6 | 0.3 | | Hidden 1 -> Output | 0.8 | 0.5 | | Hidden 2 -> Output | 0.4 | 0.6 |
Building the Neural Network in MS Excel
Now it's time to build the neural network in MS Excel:
Hidden 1 = 1 / (1 + EXP(-(Input 1 * Weight_Input1_Hidden1 + Input 2 * Weight_Input2_Hidden1 + Bias_Hidden1))) Hidden 2 = 1 / (1 + EXP(-(Input 1 * Weight_Input1_Hidden2 + Input 2 * Weight_Input2_Hidden2 + Bias_Hidden2)))
Assuming the weights and biases are in cells E2:E7, and the inputs are in cells A2:B5, the formulas would be: The update rule is: New Weight = Old
Hidden 1 = 1 / (1 + EXP(-(A2E2 + B2E4 + E3))) Hidden 2 = 1 / (1 + EXP(-(A2E5 + B2E7 + E6)))
Output = 1 / (1 + EXP(-(Hidden 1 * Weight_Hidden1_Output + Hidden 2 * Weight_Hidden2_Output + Bias_Output)))
Assuming the weights and biases are in cells E2:E7, and the hidden layer outputs are in cells C2:D5, the formula would be:
Output = 1 / (1 + EXP(-(C2E8 + D2E9 + E10)))
Training the Neural Network
To train the neural network, we need to adjust the weights and biases to minimize the error between the predicted output and the actual output. This can be done using the backpropagation algorithm.
Step 1: Calculate the Error
Calculate the error between the predicted output and the actual output:
Error = (Predicted Output - Actual Output)^2
Step 2: Backpropagate the Error
Calculate the gradients of the error with respect to each weight and bias:
dE/dWeight_Input1_Hidden1 = -2 * (Actual Output - Predicted Output) * Hidden 1 * (1 - Hidden 1) * Input 1
...and so on for each weight and bias.
Step 3: Update Weights and Biases
Update the weights and biases using the gradients and a learning rate:
Weight_Input1_Hidden1 = Weight_Input1_Hidden1 - Learning Rate * dE/dWeight_Input1_Hidden1
...and so on for each weight and bias.
Conclusion
Building a neural network with MS Excel is a feasible and educational project that can help beginners understand the basics of neural networks. While MS Excel is not the most efficient tool for large-scale neural network training, it can be used for rapid prototyping and testing of neural network architectures.
In this article, we built a simple neural network with one hidden layer to predict the output of an XOR function. We initialized the weights and biases, calculated the outputs of the hidden layer neurons, and trained the neural network using backpropagation.
Limitations and Future Work
This example is a simplified demonstration of a neural network built with MS Excel. There are several limitations and potential future work:
By following this guide, you now have a basic understanding of how to build a neural network with MS Excel. Experiment with different architectures, datasets, and optimization techniques to deepen your understanding of neural networks.
Building a neural network in Microsoft Excel is a powerful way to demystify "black box" algorithms by seeing the math in every cell. You can build a functioning network using standard formulas for Forward Propagation and Excel’s Solver tool for Backpropagation (training). 1. Structure the Architecture
A basic neural network (like one for the XOR problem or simple classification) typically needs three layers: Input Layer: Your raw data (e.g., X1cap X sub 1 X2cap X sub 2
Hidden Layer: At least 2–3 neurons to handle non-linear patterns. Output Layer: The final prediction (e.g., a 0 or 1). 2. Set Up the Weights and Biases
In a dedicated section of your spreadsheet, initialize your parameters: Weights (
): Assign a weight to every connection between neurons. Use =RAND() to start with small random values. Biases (
): Assign a bias value to each neuron in the hidden and output layers, typically initialized at 0 or a small random number. 3. Implement Forward Propagation
For each neuron, you must calculate the weighted sum and apply an activation function: Weighted Sum (
): Use the SUMPRODUCT formula to multiply inputs by their respective weights and add the bias. Formula Example: =SUMPRODUCT(Inputs, Weights) + Bias Activation Function (
): Use the Sigmoid function to squash the result between 0 and 1, allowing the network to learn complex patterns. Excel Formula: =1 / (1 + EXP(-Z)) 4. Calculate the Error (Loss)
To measure how "wrong" the network is, calculate the Mean Squared Error (MSE) for your training data. Error Per Row: =(Actual_Value - Predicted_Value)^2 Total Loss: =AVERAGE(All_Row_Errors) 5. Train the Network (Backpropagation) Neural Network in Excel Example - Drew Clark
In the age of Python, TensorFlow, and PyTorch, it is easy to forget that the core of a neural network is just matrix multiplication, activation functions, and gradient descent. Surprisingly, you can build a fully functional, trainable neural network using nothing but native Excel formulas. Repeat for all weights and biases
This guide will walk you through building a Feedforward Neural Network for the XOR logic gate problem (the "Hello World" of neural networks) without writing a single line of VBA code. You will learn how to implement Forward Propagation, Backpropagation, and Gradient Descent using only cells and formulas.