🧠 Micrograd-Mini

Micrograd-Mini is a tiny scalar-valued autograd engine with a basic neural network framework — built from scratch, inspired by Andrej Karpathy's micrograd.

It supports reverse-mode automatic differentiation (backpropagation) and is capable of training a simple neural network (MLP) on tasks like XOR. Great for learning the internals of backprop!

🔥 Features

✅ Reverse-mode autodiff (backpropagation)
✅ Dynamic computation graph (DAG)
✅ Custom Value class with gradients
✅ Tanh and ReLU activations
✅ Basic Neuron, Layer, and MLP implementation
✅ Trains on XOR dataset

📁 Project Structure

micrograd-mini/
    ├── engine.py # Core autodiff engine (Value class)
    ├── nn.py # Neural network components
    ├── train.py # Training loop for XOR
    ├── example.py # Better example using XOR dataset 
└── README.md # Project info

🚀 Getting Started

🔧 Requirements

Python 3.7+
No external libraries needed (pure Python)

▶️ Run Training

python train.py

📈 Example Output

--- Final Predictions after Training ---

Input: [0.0, 0.0] => Predicted: 0.01 | Target: 0.0
Input: [0.0, 1.0] => Predicted: 0.98 | Target: 1.0
Input: [1.0, 0.0] => Predicted: 0.97 | Target: 1.0
Input: [1.0, 1.0] => Predicted: 0.03 | Target: 0.0

Training complete! 🎯

🧠 Learn by Building

Want to really understand backpropagation and gradients?

Dive into engine.py and explore the Value class
Inspect how operations dynamically build a graph
See how .backward() traverses it for gradient computation — just like real frameworks!

🙏 Attribution

This project is heavily inspired by micrograd by Andrej Karpathy, licensed under the MIT License.

🪪 License

This project is licensed under the MIT License. See the LICENSE file for details.

✨ Author

Built with ❤️ by Muawiya, as part of a deep dive into AI, neural nets, and autodiff fundamentals.

🌐 Connect With Me

📺 YouTube: @Coding_Moves
💻 GitHub: Muawiya
🧠 LeetCode: Moavia_Amir
📊 Kaggle: Moavia Amir

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