micrograd is a minimalistic neural network library designed to facilitate an understanding of the inner workings of neural networks. This library provides a simple interface for designing neural network architectures, executing a forward pass, and performing backpropagation. It includes a small set of neurons organized into a network, and utilizes a directed acyclic graph for visualizing state propagation. This visualization aids in the step-by-step, layer-by-layer understanding of loss propagation during the backward pass.

• Design and implement simple neural network architectures.
• Perform forward passes and backpropagation.
• Visualize network state and loss propagation using directed acyclic graphs.

Before installing micrograd, ensure you have the following prerequisites installed:

• Python 3.6 or higher
• Relevant Python libraries: numpy, matplotlib for visualization (if necessary)

1. Clone the repository to your local machine:

   git clone https://github.com/[your-username]/micrograd.git
   

2. Navigate to the working directory:

   cd micrograd
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Contributions make the open-source community a fantastic place to learn, inspire, and create. Any contributions you make to this project are deeply appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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

• Name - Jay Joshi
• Email - [email protected]
• Project Link: https://github.com/jyjoshi/micrograd

• Andrej Karpathy: https://github.com/karpathy/