Here is a detailed README file for the project:

Overview

This repository contains implementations of various neural network architectures, including Transformer, GRU, LSTM, and RNN models. The goal of this project is to provide a comprehensive collection of deep learning algorithms in Python using popular libraries such as Jupyter Notebook.

Installation

To use this project, you'll need to install the required dependencies. Here are the steps:

1. Clone the repository: git clone https://github.com/tapanBabbar9/nn.git
2. Install the necessary packages:
   • Jupyter Notebook: pip install jupyter
   • TensorFlow (or other deep learning libraries): pip install tensorflow (or substitute with your preferred library)
3. Run Jupyter Notebook: jupyter notebook

Usage

Once you have installed the dependencies, you can run the notebooks in the following folders:

1. Transformer: This folder contains implementations of Transformer models.
2. GRU, LSTM, and RNN: These folders contain implementations of GRU, LSTM, and RNN models respectively.
3. Micrograd: This folder contains micro-optimizations for backpropagation.

Each notebook in these folders is designed to demonstrate a specific neural network architecture. You can run each notebook individually by clicking on the "Run" button in Jupyter Notebook.

Contribution Guidelines

We welcome contributions to this project! If you'd like to add a new neural network architecture or improve an existing one, please follow these guidelines:

1. Create a new branch: git checkout -b <branch-name>
2. Make your changes and commit them: git add . and git commit -m "Added new feature"
3. Push your changes: git push origin <branch-name>
4. Open a pull request: Go to the repository's main page, click on the "Pull requests" tab, and create a new pull request.

Repo Links

Here are some useful links for this project:

• Repository URL: https://github.com/tapanBabbar9/nn
• Issues: https://github.com/tapanBabbar9/nn/issues
• Wiki: (none)

License

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

By contributing to this project, you agree to abide by the terms of the MIT License.

Thank you for your interest in this project!