Machine Unlearning: A Neural Network Learning Backwards 🧠

What just happened here?!

This project is my humble contribution to the world of deep learning: a neural network that, for unknown reasons, learns in reverse. It starts with great accuracy and gradually forgets everything it once knew.

Essentially, I accidentally invented Machine Unlearning. A true milestone in the history of Artificial Intelligence (or lack thereof).

📸 The Code in Action

Here's how well my neural network "learns":

Epoch 1, Accuracy: 86.82%
Epoch 2, Accuracy: 87.07%
...
Epoch 20, Accuracy: 57.97%

Yes, you read that right. Each epoch makes things worse. The poor model started out strong and ended up begging for help. 🤖💔

⚙️ How to Run This Project

Clone this repository:

git clone https://github.com/your-username/machine-unlearning.git
cd machine-unlearning

Compile the C code (if you're feeling brave):

gcc -O3 -march=native -ffast-math -o nn nn.c -lm

Run it and witness the magic of unlearning:
```
./nn
```
Sit back and enjoy watching your model's performance decline.

📚 How It Works?

Honestly? Even I’m not quite sure.

Why Does This Exist?

Because making mistakes is human, but making a neural network unlearn is a rare talent.

🔗 Based On

This project is based on the amazing work at miniMNIST-c by Konrad Gajdus. I just accidentally broke it.

oblonka/machine-unlearning

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