How to Build a Working Transformer Neural Network Model in Excel

I built a complete transformer model in Excel – not a simplified version or demo, but a fully functional transformer neural network architecture using only Excel formulas and functions, proving that advanced AI can be implemented in spreadsheets.

This project pushes the boundaries of what's possible in spreadsheet computing, demonstrating that even the most advanced AI architectures – the same technology behind GPT, BERT, and other breakthrough models – can be implemented using familiar Excel tools. It's both a technical achievement and an educational breakthrough that makes AI architecture accessible to anyone who understands spreadsheets.

Using Shortcut's AI-enhanced Excel environment, this implementation shows how conversational AI can guide users through building complex models step-by-step, bridging the gap between cutting-edge AI research and practical implementation in tools millions already know.

Understanding Transformers: The Architecture Behind Modern AI

Before diving into the Excel implementation, let's understand what makes transformers revolutionary. Introduced in the 2017 paper "Attention is All You Need," transformers have become the foundation of modern natural language processing and increasingly, computer vision and other domains.

Core Components of Transformer Architecture

A transformer model consists of several key components, each of which I had to recreate in Excel:

• Self-Attention Mechanism: Allows the model to weigh the importance of different parts of the input when processing each element
• Multi-Head Attention: Multiple attention mechanisms running in parallel to capture different types of relationships
• Position Encoding: Adds information about word order since attention itself has no inherent notion of sequence
• Feed-Forward Networks: Dense neural network layers that process the attention output
• Layer Normalization: Stabilizes training by normalizing inputs to each layer
• Residual Connections: Skip connections that help with gradient flow and training stability

The Technical Challenge: Why Build AI in Excel?

Building a transformer in Excel might seem counterintuitive – why use a spreadsheet for deep learning? The answer reveals profound insights about both AI and computational tools:

Demystifying AI Through Familiar Tools

Most people view AI as a black box requiring specialized knowledge and tools. By implementing transformers in Excel, we demonstrate that:

• AI is fundamentally mathematical operations that can be expressed in any computational environment
• Complex neural networks are composed of simple, understandable operations
• The "magic" of AI comes from scale and optimization, not mysterious processes
• Anyone who understands Excel formulas can understand neural network operations

Educational Value and Accessibility

Excel's visual, interactive nature makes it an ideal platform for learning AI concepts:

• See exactly how data flows through each layer
• Modify parameters and immediately observe effects
• Step through calculations cell by cell
• No programming knowledge required
• Accessible to millions of Excel users worldwide

Implementation Deep Dive: Building Each Component

Creating a transformer in Excel required innovative use of formulas and functions to replicate neural network operations. Here's how each component was implemented:

1. Token Embeddings and Vocabulary

The first challenge was representing words as numbers that Excel could process:

Each word in the vocabulary maps to a unique vector of numbers, typically 512 or 768 dimensions in production models. In Excel, I used 64 dimensions for computational feasibility while maintaining the essential architecture.

2. Positional Encoding with Trigonometric Functions

Transformers need to know word positions since attention mechanisms are position-agnostic. The original paper uses sinusoidal position encodings:

In Excel, this translated to:

• SIN and COS functions for alternating dimensions
• POWER function for the scaling factor
• Array formulas to compute across all positions simultaneously
• Dynamic ranges to handle variable sequence lengths

3. Multi-Head Attention Mechanism

The attention mechanism is the heart of transformers. Implementing it in Excel was the most complex challenge:

Excel implementation required:

1. Query, Key, Value Matrices: Created using MMULT for matrix multiplication
2. Scaled Dot-Product: SUMPRODUCT for dot products, SQRT for scaling
3. Softmax Function: EXP for exponentials, SUM for normalization
4. Multiple Heads: Separate calculation ranges for each attention head
5. Concatenation: CONCATENATE and array formulas to combine heads

4. Feed-Forward Neural Networks

Each transformer layer includes a feed-forward network with two linear transformations and a ReLU activation:

• First linear layer: MMULT with weight matrix W1
• ReLU activation: MAX(0, value) for each element
• Second linear layer: MMULT with weight matrix W2
• Dropout simulation: RAND() function with IF statements

5. Layer Normalization

Normalizing layer inputs improves training stability. In Excel:

• AVERAGE function for mean calculation
• STDEV.P for standard deviation
• Normalized values: (X - mean) / (std + epsilon)
• Learnable parameters gamma and beta for scaling and shifting

6. Residual Connections

Skip connections were surprisingly straightforward in Excel:

• Simple addition of input and output ranges
• Cell references maintaining the connection structure
• Helps visualize gradient flow in the network

Performance and Optimization Challenges

Running a neural network in Excel presents unique performance challenges:

Computational Limitations

• Matrix Size Limits: Excel's 1,048,576 rows and 16,384 columns constrain model size
• Calculation Speed: Complex formulas across thousands of cells can be slow
• Memory Usage: Large models can exceed Excel's memory limits
• Precision: Floating-point limitations affect numerical stability

Optimization Techniques

To make the transformer practical in Excel, I employed several optimizations:

• Sparse Matrices: Only store non-zero values to save memory
• Efficient Formulas: Replace complex nested formulas with helper columns
• Manual Calculation: Control when recalculation occurs
• Modular Design: Separate worksheets for different components
• Caching: Store intermediate results to avoid redundant calculations

Real-World Applications and Use Cases

While this Excel transformer is primarily educational, it has surprising practical applications:

Educational Tool for AI Understanding

• Universities using it to teach neural network concepts
• Corporate training programs for AI literacy
• Self-learners exploring AI without programming
• Demonstrating AI concepts to non-technical stakeholders

Prototyping and Experimentation

• Quick testing of architectural modifications
• Visualizing attention patterns for specific inputs
• Understanding model behavior through step-by-step execution
• Debugging neural network logic

Interpretable AI Research

The transparency of Excel implementation aids interpretability research:

• Every calculation is visible and traceable
• No hidden states or black box operations
• Easy to probe intermediate representations
• Helps develop intuition about model behavior

Building Your Own Transformer in Excel with Shortcut

Shortcut makes building complex models like transformers accessible through conversational AI. Here's how you can create your own:

Step-by-Step Guide

1. Start with Shortcut: Open Shortcut and create a new Excel workbook
2. Request Architecture: "Create a transformer model architecture with attention mechanism"
3. Build Components: Ask Shortcut to implement each component sequentially
4. Connect Layers: "Connect the attention output to feed-forward network"
5. Add Visualizations: "Create charts showing attention weights"
6. Test and Iterate: Input sample data and observe outputs

Example Prompts for Shortcut

Lessons Learned and Insights

Building a transformer in Excel revealed several important insights:

AI Demystification

• Neural networks are deterministic mathematical operations, not magic
• Complex behaviors emerge from simple repeated operations
• The "intelligence" comes from scale and learned parameters
• Understanding the mechanics helps appreciate both capabilities and limitations

Excel's Hidden Power

• Excel is more powerful than most people realize
• Complex mathematical models are possible with creative formula use
• Visual nature aids understanding of abstract concepts
• Accessibility makes advanced concepts available to non-programmers

The Bigger Picture: Democratizing AI Development

This project represents more than just a technical achievement – it's about democratizing AI development and education. When advanced neural networks can be built and understood using familiar tools, we remove barriers to AI literacy and experimentation.

It demonstrates that the principles underlying modern AI are fundamentally mathematical operations that can be expressed in many different computational environments. Excel just happens to be one that millions of people already know how to use.

With tools like Shortcut making complex Excel operations accessible through natural language, the gap between cutting-edge AI research and practical implementation continues to narrow. Anyone with curiosity and Excel can now explore the architectures powering the AI revolution.

Ready to push the boundaries of what's possible in Excel? Explore the intersection of spreadsheets and AI with Shortcut.