# imagineailab/ai-by-hand-excel

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6,177 stars · 765 forks · MIT

## Links

- GitHub: https://github.com/ImagineAILab/ai-by-hand-excel
- awesome-repositories: https://awesome-repositories.com/repository/imagineailab-ai-by-hand-excel.md

## Description

This project consists of interactive spreadsheet-based models designed to demonstrate the mathematical mechanics of backpropagation, multi-layer perceptrons, and transformer attention. It serves as an Excel-based neural network simulator for manually calculating tensor operations and matrix multiplications to visualize data flow.

The models provide a way to visualize the internal logic of neural networks by implementing self-attention and backpropagation through explicit cell formulas. It includes specific mathematical exercises for modeling transformer architecture and the layers of multi-layer perceptrons.

The simulation covers deep learning fundamentals, including the calculation of forward passes and the use of gradient descent to update weights. It uses cell-based tensor simulations to track parameter updates and map weighted relationships within attention mechanisms.

## Tags

### Education & Learning Resources

- [Spreadsheet AI Simulations](https://awesome-repositories.com/f/education-learning-resources/spreadsheet-ai-simulations.md) — Builds simplified versions of multi-layer perceptrons in Excel to visualize internal neural network behavior.
- [Deep Learning Fundamentals](https://awesome-repositories.com/f/education-learning-resources/deep-learning-curriculum/deep-learning-fundamentals.md) — Provides practical exercises on backpropagation and matrix multiplication to teach core deep learning logic.
- [Neural Network Forward Pass Simulators](https://awesome-repositories.com/f/education-learning-resources/interactive-learning-tools/neural-network-forward-pass-simulators.md) — Provides an interactive simulation of the step-by-step arithmetic involved in a neural network forward pass.
- [Neural Network Simulators](https://awesome-repositories.com/f/education-learning-resources/neural-network-simulators.md) — Provides a spreadsheet-based environment for manually calculating tensor operations to visualize neural network data flow.

### Artificial Intelligence & ML

- [Attention Visualizations](https://awesome-repositories.com/f/artificial-intelligence-ml/attention-visualizations.md) — Visualizes self-attention weights and relationships through matrix multiplication cells in a spreadsheet.
- [Backpropagation Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/architectures/neural-network-components/backpropagation-implementations.md) — Implements gradient-based optimization logic through explicit formulas to demonstrate how weights are updated.
- [Educational Neural Network Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/educational-neural-network-implementations.md) — Offers pedagogical implementations of neural network components built from first principles using spreadsheet calculations.
- [Simulated Transformer Models](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/transformer-architectures/simulated-transformer-models.md) — Simulates self-attention and the mathematical operations of transformer networks using manual Excel calculations.
- [Transformer Architecture Visualizations](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-architecture-visualizations.md) — Maps the internal mathematical operations and data flow of transformer architectures using cell-based calculations.
- [MLP Mathematical Exercises](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers/convolution-layers/layered-architectures/multi-layer-perceptrons/mlp-mathematical-exercises.md) — Offers a set of manual calculations in a spreadsheet for modeling the layers and weights of a multi-layer perceptron.
- [Educational Tensor Operations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/tensor-computing-libraries/tensor-libraries/mathematical-operations/educational-tensor-operations.md) — Provides tools for calculating core tensor operations and matrix multiplications to track neural network data flow. ([source](https://github.com/imagineailab/ai-by-hand-excel#readme))
- [Reference-Linked Updates](https://awesome-repositories.com/f/artificial-intelligence-ml/model-weight-management/dynamic-weight-updates/reference-linked-updates.md) — Updates network parameters by linking output error cells directly back to input weight cells.
- [Mathematical Architecture Modeling](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-architectures/mathematical-architecture-modeling.md) — Models the mathematical operations of transformers and multi-layer perceptrons using manual calculations. ([source](https://github.com/imagineailab/ai-by-hand-excel#readme))

### Scientific & Mathematical Computing

- [Cell-Based Tensor Simulations](https://awesome-repositories.com/f/scientific-mathematical-computing/cell-based-tensor-simulations.md) — Implements tensor elements and linear algebra operations using individual spreadsheet cells for visualization.

### Business & Productivity Software

- [Spreadsheet Parameter Tracking](https://awesome-repositories.com/f/business-productivity-software/spreadsheet-parameter-tracking.md) — Stores model weights and biases in static cell ranges for manual adjustment and observation.