ImagineAILab/ai-by-hand-excel
Ai By Hand Excel
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.
Features
- Spreadsheet AI Simulations - Builds simplified versions of multi-layer perceptrons in Excel to visualize internal neural network behavior.
- Attention Visualizations - Visualizes self-attention weights and relationships through matrix multiplication cells in a spreadsheet.
- Backpropagation Implementations - Implements gradient-based optimization logic through explicit formulas to demonstrate how weights are updated.
- Educational Neural Network Implementations - Offers pedagogical implementations of neural network components built from first principles using spreadsheet calculations.
- Simulated Transformer Models - Simulates self-attention and the mathematical operations of transformer networks using manual Excel calculations.
- Transformer Architecture Visualizations - Maps the internal mathematical operations and data flow of transformer architectures using cell-based calculations.
- Deep Learning Fundamentals - Provides practical exercises on backpropagation and matrix multiplication to teach core deep learning logic.
- Neural Network Forward Pass Simulators - Provides an interactive simulation of the step-by-step arithmetic involved in a neural network forward pass.
- Neural Network Simulators - Provides a spreadsheet-based environment for manually calculating tensor operations to visualize neural network data flow.
- Cell-Based Tensor Simulations - Implements tensor elements and linear algebra operations using individual spreadsheet cells for visualization.
- MLP Mathematical Exercises - Offers a set of manual calculations in a spreadsheet for modeling the layers and weights of a multi-layer perceptron.
- Educational Tensor Operations - Provides tools for calculating core tensor operations and matrix multiplications to track neural network data flow.
- Reference-Linked Updates - Updates network parameters by linking output error cells directly back to input weight cells.
- Mathematical Architecture Modeling - Models the mathematical operations of transformers and multi-layer perceptrons using manual calculations.
- Spreadsheet Parameter Tracking - Stores model weights and biases in static cell ranges for manual adjustment and observation.
Star history
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