Click , go to the GRG Nonlinear tab, and ensure Use Multistart is checked. This helps the network escape local minima to find global weights. Click Solve .
: Python results can be returned directly to cells as dynamic arrays, making real-time predictions easy.
Artificial Intelligence (AI) often feels like a black box restricted to Python, massive GPUs, and specialized data scientists. However, the paradigm is shifting. In 2026, —equipped with modern, powerful features—has transformed into a capable, accessible sandbox for understanding and building machine learning models.
Excel's optimization engine will rapidly iterate through the math, adjusting your weights and biases until the Total Network Loss drops near zero. build neural network with ms excel new
To introduce non-linearity, apply the ReLU function, which keeps positive values and sets negative values to zero. Use this formula: =MAX(0, Weighted_Sum) . Step 2: Calculate Output Layer
Set up your weight cells to reference themselves minus the gradient: =IF(Reset_Cell=TRUE, RAND(), Current_Weight_Cell - (Learning_Rate * Gradient_Cell))
Instead of passing a single row vector, pass a complete Click , go to the GRG Nonlinear tab,
Use Python PY() in a cell to apply the sigmoid function
This year, the AI community was stunned by an incredible feat: a computer science professor built a complete AI model—a state-of-the-art sequence model designed as a competitor to the Transformer—inside an Excel spreadsheet. This was not a "toy" model; it was a working implementation, packed into a file of just 322KB, capable of performing calculations for four-dimensional inputs. Professor Tom Yeh's work is a stunning testament to the hidden power of Excel and a perfect example of using the tool to deconstruct and teach the most advanced AI concepts.
Set to your Weight and Bias blocks ( Weights_1, Bias_1, Weights_2, Bias_2 ). Select GRG Nonlinear as the solving method. : Python results can be returned directly to
Building a neural network in Excel shows that AI is fundamentally about weighted sums and non-linear transformations. While Python is essential for large, complex datasets, modern Excel provides an excellent, transparent, and immediate playground for learning, prototyping, and solving simple machine learning problems. If you'd like to dive deeper, let me know: Are you interested in using for automation?
Historically, building a neural network in Excel required clunky VBA macros. Today, the landscape is different:
Sigmoid(x)=11+e−xSigmoid open paren x close paren equals the fraction with numerator 1 and denominator 1 plus e raised to the negative x power end-fraction In Excel: =1 / (1 + EXP(-Final_Sum)) . 5. Training the Network (The "New" Excel Part)
Go to the tab and click Solver . (If missing, enable it via File -> Options -> Add-ins). Set the Objective Cell to your Total Loss cell. Set the optimization goal to Min (Minimize).
We will build a . Specifically, we will create a neural network that can learn the XOR Logic Gate (Exclusive OR).