A set of Deep Learning Architectures implemented using PyTorch with explanations.
Welcome to the Neural Network Architectures repository! This project aims to demystify various neural network architectures by providing detailed implementations and explanations in Jupyter Notebooks, accompanied by their original research papers and additional resources.
This repository contains a collection of Jupyter Notebooks, each dedicated to a specific neural network architecture. These notebooks not only implement the networks in PyTorch but also delve into the theories, intuitions, and details behind each architecture, making it an invaluable resource for both learning and teaching.
- Detailed Implementations: Each neural network architecture is implemented in PyTorch within a Jupyter Notebook.
- Comprehensive Explanations: Accompanying each implementation is a thorough explanation, including the network's foundational theory, practical applications, and a walkthrough of the code.
- Research Papers: Direct links to the original research papers are provided for further reading and deeper understanding.
- Interactive Learning: By leveraging Jupyter Notebooks, users can interact with the code, experiment with different parameters, and see the results in real time.
To get started with this repository:
- Clone the repository:
git clone https://github.com/alialhousseini/NeuroNovice.git
- Install the required dependencies:
pip install -r requirements.txtWe welcome contributions to this repository! Whether it's adding new architectures, enhancing existing notebooks, or improving documentation, your help is appreciated. To contribute:
- Fork the repository.
- Create a new branch for your feature (git checkout -b feature-branch).
- Make your changes and commit them (
git commit -am 'Add some feature'). - Push to the branch (git push origin feature-branch).
- Create a new Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.