Build Neural Network With Ms Excel New !!better!! May 2026

Build Neural Network with MS Excel New: A Step-by-Step Guide to Deep Learning Without Code

In the modern era of artificial intelligence, it seems like you need a PhD in mathematics, a powerful GPU cluster, and fluency in Python (TensorFlow or PyTorch) to build a neural network. However, a quiet revolution has occurred. You can now build a fully functional, learning neural network using the "new" dynamic array features of Microsoft Excel.

Forget the old days of clunky VBA macros or manual matrix multiplication. With Excel’s dynamic arrays (FILTER, SORT, RANDARRAY) and modern LAMBDA functions, you can build a Forward Propagation and Backpropagation engine from scratch inside a spreadsheet.

In this guide, we will build a 3-layer perceptron (Input → Hidden → Output) capable of learning the XOR logic gate—a classic problem that proves non-linear learning. By the end, you will have a living Excel model that "learns" in front of your eyes.

The Three Lessons I Learned

1. Backprop is just addition and multiplication. Excel has no autograd. Writing dLoss/dW = (Pred - True) * Input manually makes you realize that deep learning is simply weighted averages with memory.

2. Local minima are visible. In Python, loss curves are abstract plots. In Excel, you watch the "Loss" cell bounce up and down as you tap F9. You can see the model get stuck. You can see it escape.

3. Excel is the ultimate low-code ML platform. For a business analyst who cannot install Python, a simple logistic regression (1-neuron network) in Excel is incredibly powerful. Adding a hidden layer is overkill, but it proves that the barrier to AI is no longer code—it is understanding.

Troubleshooting

| Problem | Likely fix | |----------------------------------|------------------------------------------| | #VALUE! in MMULT | Check matrix dimensions. W1: 2x2, Input: 4x2. | | Loss not decreasing | Increase iterations in Solver; re-initialize weights. | | Predictions stuck near 0.5 | Increase weight range (try -1 to +1 initially). | | Solver not found | Enable Solver Add-in (File → Options → Add-ins). |

The Architecture (The "New" Way)

We will build a network with:

Inputs: 2 neurons (X1, X2)
Hidden Layer: 4 neurons (with ReLU activation)
Output Layer: 1 neuron (with Sigmoid activation)
Loss Function: Mean Squared Error (MSE)

We will train it using Stochastic Gradient Descent (SGD) .

Step-by-step: Build a simple feedforward neural network in Microsoft Excel (newer Excel versions)

This guide produces a working, trainable 1-hidden-layer neural network (input → hidden → output) that you can run, inspect, and train with backpropagation using only Excel formulas and built-in tools (no add-ins). Assumptions and defaults:

Network: 2 inputs, 3 hidden neurons, 1 output (you can change sizes).
Activation: sigmoid for hidden and output.
Loss: mean squared error (MSE).
Training method: batch gradient descent with learning rate you set.
Excel: modern Excel with dynamic arrays and standard functions (works with older too with small formula adjustments).

Overview of sheets and layout

Sheet "Params": topology and hyperparameters.
Sheet "Data": training examples (X, y).
Sheet "Weights": weights and biases.
Sheet "Forward": forward pass computations (activations).
Sheet "Grad": gradients and weight updates (backprop).
Sheet "Train": training loop controls (iteration counter, loss history).

Create the workbook and sheets

Add sheets named: Params, Data, Weights, Forward, Grad, Train.

Params sheet (single-cell hyperparams)

A1: "Input size" → B1: 2
A2: "Hidden size" → B2: 3
A3: "Output size" → B3: 1
A4: "Learning rate" → B4: 0.5
A5: "Epochs / iterations" → B5: 1000
A6: "Batch size" → B6: (set to number of rows in Data or leave blank for full-batch)

Data sheet (training set)

Row1 headers: X1, X2, Y
Rows 2..n: put training examples. Example: XOR dataset
- Row2: 0, 0, 0
- Row3: 0, 1, 1
- Row4: 1, 0, 1
- Row5: 1, 1, 0
Use an Excel Table (Insert → Table) named "TrainData" to make dynamic ranges easier; otherwise refer to ranges explicitly.

Weights sheet (initialize weights & biases)

Layout for readability; use ranges:
- W1: weights from input → hidden: create a 2×3 table (rows = inputs, cols = hidden units).
  - Put header row "h1","h2","h3" and left column "x1","x2".
  - Fill cells with small random numbers: =NORM.INV(RAND(),0,0.5) or =RAND()*0.2-0.1
- b1: hidden biases: a 1×3 row next to W1, initialize =0 or small random.
- W2: weights from hidden → output: a 3×1 column, initialize small random.
- b2: output bias: single cell.
Example cell addresses (adjust if you place differently):
- W1 in Weights!B2:D3 (2 rows × 3 cols)
- b1 in Weights!B4:D4 (1 × 3)
- W2 in Weights!F2:F4 (3 × 1)
- b2 in Weights!F5

Tip: Lock initial random seed by replacing RAND() with fixed numbers if you want reproducible runs.

Forward pass: compute activations for each training example

Use the Data sheet rows as inputs and vectorized formulas to compute network outputs.
Create header row on Forward: X1, X2, Z1_h1, Z1_h2, Z1_h3, A1_h1, A1_h2, A1_h3, Z2, A2, Error, SqError
For each training row (example in Data row i), compute:
- Z1_j = sum_k (X_k * W1_kj) + b1_j
  - In Excel, if inputs are in Data!A2:B2, and W1 in Weights!B2:D3 and b1 in Weights!B4:D4, compute hidden pre-activations with:
    - =MMULT(TRANSPOSE(Data!A2:B2), Weights!B2:D3) + Weights!B4:D4
    - If MMULT returns an array, Excel will spill into three cells for Z1_h1..Z1_h3.
- A1_j = sigmoid(Z1_j) where sigmoid(z)=1/(1+EXP(-z)).
  - Example: =1/(1+EXP(-cell_with_Z1))
- Z2 = SUMPRODUCT(A1_range, W2_range) + b2
  - Example: =SUMPRODUCT( A1_spill_range, Weights!F2:F4 ) + Weights!F5
- A2 = sigmoid(Z2)
- Error = y - A2
- SqError = (Error)^2
If you use tables and spill formulas you can compute forward pass for all rows at once using array formulas referencing entire Data ranges and MMULT.

Loss (MSE)

On Forward or Train sheet compute MSE over all training examples:
- =AVERAGE(Forward!SqError_range)

Backpropagation: compute gradients row-wise and average

For each example compute gradients for each weight and bias; then average across batch (full-batch):
- For output layer:
  - dA2 = A2 - y (derivative of MSE w.r.t activation; if using 1/2 factor, adjust)
  - dZ2 = dA2 * sigmoid'(Z2) where sigmoid'(z)=sigmoid(z)*(1-sigmoid(z)) → dZ2 = dA2 * A2 * (1-A2)
  - dW2_j = A1_j * dZ2
  - db2 = dZ2
- For hidden layer:
  - dA1_j = dZ2 * W2_j (propagate error back)
  - dZ1_j = dA1_j * A1_j * (1-A1_j)
  - dW1_kj = X_k * dZ1_j
  - db1_j = dZ1_j
Implementation tips in Excel:
- In sheet "Grad", mirror Forward rows and compute dZ2, dW2, db2, dZ1s, dW1s per example using cell formulas and SUMPRODUCT.
- Example formulas (adjust ranges):
  - dA2_cell = Forward!A2_output - Forward!Y
  - dZ2_cell = dA2_cell * Forward!A2 * (1-Forward!A2)
  - dW2_range_for_example = Forward!A1_range * dZ2_cell (produces 3 values)
  - dZ1_range = dZ2_cell * Weights!F2:F4 * Forward!A1_range * (1-Forward!A1_range) — compute element-wise
  - dW1_matrix = X_vector (2×1) * dZ1_row (1×3) → use outer product to get 2×3 matrix:
    - If X_vector is in cells, use: =MMULT(TRANSPOSE(X_vector), dZ1_row) to produce 2×3 array
- Use array-aware functions or copy formulas per column if dynamic arrays unavailable.

Aggregate gradients and update weights (batch)

Average gradients across all examples (use AVERAGE across rows for each weight cell).
Update rule: W_new = W_old - learning_rate * grad_W_avg
- In Weights sheet, create adjacent cells for Updated_W1, Updated_b1, Updated_W2, Updated_b2 with formulas:
  - =W1_cell - Params!B4 * Grad!Avg_dW1_cell
  - =b1_cell - Params!B4 * Grad!Avg_db1_cell
To perform iterative training, you have two approaches: A) Manual update via copy–paste
- After computing Updated_W* formulas, copy Updated_* cells and Paste Values back into W* cells to apply update, then recalc until epochs completed. B) Use a macro (VBA) to loop and apply updates automatically (recommended for many epochs).
- Minimal VBA outline:
```
Sub TrainNN()
  Dim epochs As Long: epochs = Sheets("Params").Range("B5").Value
  Dim lr As Double: lr = Sheets("Params").Range("B4").Value
  Dim i As Long
  For i = 1 To epochs
    Sheets("Forward").Calculate
    Sheets("Grad").Calculate
    ' read averages from Grad sheet, update Weights values
    ' example: W1(1,1) = W1(1,1) - lr * GradAvg_W1(1,1)
    ' update all weight cells similarly
  Next i
End Sub
```
- Use Application.Calculate or CalculateFull if needed. Save before running macros.

Debugging and verification

Start with a tiny learning rate (0.1–1.0 for simple tasks) and few epochs to see loss decreasing.
Print loss each epoch in Train sheet: have a cell that records iteration number and MSE (use VBA to append).
Test on simple problems (XOR requires nonlinearity and hidden layer — good test).
If loss diverges: reduce learning rate, initialize weights smaller, or use normalization on inputs.

Extensions and improvements

Use tanh activation by replacing sigmoid formulas (tanh(z)=TANH(z), derivative = 1 - tanh(z)^2).
Implement momentum: maintain velocity matrices and update weights with v = muv - lrgrad; W += v.
Implement mini-batch: compute averages per batch subset.
Add regularization: L2 by subtracting lr * lambda * W during update.
Use Excel charts to plot loss vs iterations (Train sheet).

Example workbook hints (practical)

Keep consistent named ranges: Inputs = Data!A2:B5, Targets = Data!C2:C5, W1 = Weights!B2:D3, b1 = Weights!B4:D4, W2 = Weights!F2:F4, b2 = Weights!F5.
Use helper columns to keep formulas clear (Z1_1, A1_1, …).
When using MMULT, ensure dimensions match and wrap in N() or VALUE() when needed.

Minimal numeric example (XOR)

Use the XOR Data rows given in step 3.
Initialize W1 small randoms like:
- W1 = [[0.2, -0.3, 0.1],[0.4, 0.25, -0.2]], b1 = [0,0,0]
- W2 = [0.3, -0.1, 0.2], b2 = 0
Run a few hundred epochs with lr=0.5 — you should see MSE reduce and outputs approach targets.

Saving and reproducibility

After training, paste values of weight cells to freeze final model.
Save workbook; include a sheet that documents final weights and a test table to validate predictions.

If you want, I can:

Produce exact cell-by-cell formulas for your workbook layout (tell me whether you prefer 0-indexed or 1-indexed rows, exact cell addresses, and whether you want VBA included), or
Generate a ready-to-download Excel file with the network implemented (requires file export capability).

Building a Neural Network with MS Excel: A Comprehensive Review build neural network with ms excel new

Introduction

Microsoft Excel is a widely used spreadsheet software that is often underestimated for its capabilities beyond basic data analysis and visualization. However, with the right techniques and add-ins, Excel can be transformed into a powerful tool for building and training neural networks. In this review, we'll explore the process of building a neural network with MS Excel, focusing on the "new" approach.

The "New" Approach: What's Changed?

The "new" approach refers to the updated methods and tools available in MS Excel for building neural networks. With the introduction of new add-ins, such as the "Power BI" and "Excel Power Query Editor", users can now leverage advanced data analysis and machine learning capabilities. Additionally, Excel's built-in functions, like LINEST and LOGEST, have been improved to support more complex calculations.

Step-by-Step Guide to Building a Neural Network in MS Excel

Data Preparation: Collect and preprocess your data, ensuring it's clean and organized. Excel's data manipulation tools, such as filtering and sorting, make this process efficient.
Neural Network Add-ins: Install and enable the necessary add-ins, like "Neural Network" or "Power BI", to access neural network functionality.
Network Design: Define your neural network architecture, including the number of inputs, hidden layers, and outputs. Excel's flexibility allows for easy experimentation with different architectures.
Training: Use Excel's built-in optimization functions, like Solver, to train your neural network. You can also utilize third-party add-ins for more advanced training algorithms.
Testing and Evaluation: Assess your neural network's performance using metrics like mean squared error (MSE) or R-squared.

Pros and Cons of Building a Neural Network in MS Excel

Pros:

Accessibility: Excel is widely available, making it an excellent choice for those already familiar with the software.
Ease of use: Excel's intuitive interface and visual tools simplify the process of building and testing neural networks.
Rapid Prototyping: Excel's flexibility enables quick experimentation and testing of different neural network architectures.

Cons:

Limited Scalability: As your neural network grows in complexity, Excel may become less efficient and more prone to errors.
Limited Advanced Features: Compared to dedicated deep learning frameworks like TensorFlow or PyTorch, Excel's neural network capabilities are limited.

Conclusion

Building a neural network with MS Excel is a viable option for those looking to dip their toes into machine learning or for projects that don't require extreme complexity. The "new" approach offers improved tools and functionality, making it easier to get started. While Excel may not replace specialized deep learning frameworks, it provides a unique combination of accessibility and ease of use.

Rating: 4/5 stars

Recommendation:

Use MS Excel for:
- Small-scale neural network projects
- Rapid prototyping and testing
- Educational purposes
Consider alternative tools for:
- Large-scale neural network projects
- Advanced features and customization

By following this review, you should now have a better understanding of the possibilities and limitations of building a neural network with MS Excel using the "new" approach. Happy building! Build Neural Network with MS Excel New: A

Introduction

Microsoft Excel is a widely used spreadsheet software that can be used for various tasks, including data analysis and visualization. While it's not a traditional tool for building neural networks, Excel can be used to create simple neural networks using its built-in functions and tools. In this report, we'll explore how to build a basic neural network using MS Excel.

Neural Network Basics

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" that process and transmit information. The three main types of layers in a neural network are:

Input Layer: receives data
Hidden Layer: processes data
Output Layer: generates predictions

Building a Neural Network in MS Excel

To build a neural network in MS Excel, we'll use the following steps:

Prepare the Data: collect and preprocess the data you want to use for training and testing the neural network. For this example, let's assume we have a simple dataset with two input variables (X1 and X2) and one output variable (Y).
Create the Neural Network Structure: create a new spreadsheet with the following layout:
- Input Layer: two cells for X1 and X2
- Hidden Layer: two cells for the hidden neurons (H1 and H2)
- Output Layer: one cell for the output (Y)
Define the Activation Functions: define the activation functions for the hidden and output layers. For simplicity, let's use the sigmoid function: $$sigmoid(x) = \frac11+e^-x$$
Calculate the Weights and Biases: initialize the weights and biases for the connections between layers. For this example, let's assume the following weights and biases:
- W1 (X1 to H1): 0.5
- W2 (X2 to H1): 0.3
- W3 (X1 to H2): 0.2
- W4 (X2 to H2): 0.4
- B1 (H1): 0.1
- B2 (H2): 0.2
Calculate the Hidden Layer Outputs: calculate the outputs of the hidden layer using the inputs, weights, and biases:
- H1 = sigmoid(X1W1 + X2W2 + B1)
- H2 = sigmoid(X1W3 + X2W4 + B2)
Calculate the Output Layer Output: calculate the output of the output layer using the hidden layer outputs, weights, and biases:
- Y = sigmoid(H1W5 + H2W6 + B3)
Train the Neural Network: use the Solver tool in MS Excel to minimize the error between the predicted output and the actual output. For this example, let's assume a mean squared error (MSE) objective function.

Example Calculations

Suppose we have the following input data:

| X1 | X2 | Y | | --- | --- | --- | | 0.5 | 0.3 | 0.8 |

Using the weights and biases defined above, we can calculate the hidden layer outputs:

H1 = sigmoid(0.50.5 + 0.30.3 + 0.1) = 0.62
H2 = sigmoid(0.50.2 + 0.30.4 + 0.2) = 0.55

Then, we can calculate the output layer output:

Y = sigmoid(0.620.6 + 0.550.7 + 0.3) = 0.82

Conclusion

Building a neural network in MS Excel is a feasible task, although it may not be the most efficient or scalable approach. By using Excel's built-in functions and tools, you can create a simple neural network that can learn from data. However, for more complex neural networks or larger datasets, you may want to consider using specialized machine learning software or libraries. The Three Lessons I Learned 1

Limitations and Future Work

This example demonstrates a basic neural network with a single hidden layer. However, there are many ways to improve and extend this model, such as:

Adding more hidden layers or neurons
Using different activation functions
Regularization techniques
Larger datasets

I hope this report provides a helpful starting point for building neural networks with MS Excel. If you have any questions or need further clarification, feel free to ask!

Here are some key takeaways in bullet points:

MS Excel can be used to build simple neural networks
A basic neural network structure consists of input, hidden, and output layers
Activation functions, weights, and biases are crucial components of a neural network
The Solver tool in MS Excel can be used to train the neural network
This approach has limitations, but can be a useful starting point for simple neural networks.

Building a Simple Neural Network with Microsoft Excel

Microsoft Excel is a widely used spreadsheet software that can be used for various tasks, including data analysis and visualization. While it's not a traditional choice for building neural networks, Excel can be used to create a simple neural network using its built-in functions and tools. In this article, we'll explore how to build a basic neural network using Microsoft Excel.

Neural Network Basics

Input Layer: receives input data
Hidden Layer: performs complex calculations on the input data
Output Layer: generates the final output

Setting up the Neural Network in Excel

To build a simple neural network in Excel, we'll use the following steps:

Step 3: Calculate Loss (Error)

We use Mean Squared Error (MSE).

In a cell:

=AVERAGE((Predictions - TargetData)^2)

Name this Loss.

Initially, with random weights, loss will be ~0.25 (chance level). Your goal: reduce loss to <0.01.

Step 2.1: Hidden Layer Linear Sum (Z1)

In cell F6 (using dynamic array multiplication MMULT): =MMULT(Input, W1) + B1 Result: A 1x4 array. The MMULT function is the native matrix multiplier.

Step 4.1: Enable Iterative Calculation

Go to File > Options > Formulas.
Check Enable iterative calculation.
Set Maximum Iterations to 1. (We will copy down the sheet to simulate epochs).
Set Maximum Change to 0.0001.