🧠 MNIST Neural Network Simulator – Minecraft Edition

This project is a hybrid of machine learning and redstone engineering: a fully simulated neural network trained on the MNIST handwritten digits dataset, with the ultimate goal of recreating its behavior within Minecraft using redstone logic.

Inspired by the groundbreaking work of MattBatWings, this project aims to bridge the gap between educational AI tools and Minecraft's mechanical sandbox.

🎯 Project Goal

Simulate a basic neural network architecture that can classify MNIST digits. Visualize and translate the network's logic into redstone-compatible circuits. Recreate the entire pipeline (input, weights, activation, output) using Minecraft mechanics like redstone torches, comparators, pistons, and observers.

🛠️ Technologies Used

• Python (NumPy, Matplotlib)
• MNIST Dataset (28x28 grayscale images)
• Feedforward Neural Network Architecture
• Manual backpropagation (for redstone logic compatibility)
• ASCII and pixel-to-block map visualization
• Minecraft Java Edition (Creative Mode with redstone building)

🧠 Architecture

• Input Layer: 784 nodes (28x28 grayscale)
• Hidden Layer(s): Customizable, default 16–32 neurons
• Output Layer: 10 nodes (digits 0–9)
• Activation Function: Sigmoid
• Loss Function: Mean Squared Error
• Optimizer: Manual backpropagation to simulate redstone compatibility

🧪 How to Run the Simulation

Clone the repo:

git clone https://github.com/your-username/minecraft-mnist-sim.git
cd minecraft-mnist-sim