Here is what I learned about the soul of machine learning.
Close the VBA window, link this macro to a shape button on your spreadsheet, and click it. You will watch your drop rapidly with every iteration as the network learns the underlying patterns of your data. Why Build a Neural Network in Excel?
. This allows you to use industry-standard libraries directly in a cell without leaving the application. function to open a Python editor in any cell. : You can import Scikit-learn TensorFlow/Keras build neural network with ms excel new
To update weights, you need the gradient. For Sigmoid: =Sigmoid_Cell * (1 - Sigmoid_Cell)
In Excel, you can simulate one iteration per row, or you can manually copy and paste the updated parameters over the initial ones to run another epoch. With each iteration, you should see the total error decrease as the network slowly learns the mapping from inputs to outputs. Here is what I learned about the soul of machine learning
you can now build and run sophisticated neural networks using several new and integrated features available as of early 2026 1. Python in Excel (Recommended) The most powerful way to build a neural network is via the Python in Excel integration. How it works function to write actual Python code directly in cells. : You can import industry-standard libraries like TensorFlow to define and train models within your spreadsheet. : Prepare your data in a range, use Python to train a Sequential model, and output predictions back into cells. 2. Azure Machine Learning Functions
"You need a GPU, a cloud instance, or at least a Jupyter notebook to train a neural network." Why Build a Neural Network in Excel
We spend our lives abstracting away complexity. Sometimes, the best way to learn is to go back to the grid—the original tensor—and build it by hand.
But simpler: extend InputData to include new row and recalc.
Since MMULT() is volatile, we use =SUMPRODUCT(weights_range, input_range) .
Multiply the inputs by the first weight matrix and add the first bias vector.