Deep-Paquito is a lightweight deep learning framework developed from scratch in C++. This project is a personal endeavor aimed at gaining a deeper understanding of machine learning and deep learning principles. The framework is intentionally kept small to serve as a learning tool, making it accessible for anyone interested in exploring the inner workings of deep learning algorithms.
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Modular Design: Deep-Paquito is designed with a modular architecture, allowing users to easily understand and modify individual components.
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From Scratch Implementation: Every aspect of the framework, including neural network layers, activation functions, and optimization algorithms, is implemented from scratch, providing a hands-on learning experience.
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C++ Implementation: The entire framework is written in C++, making it suitable for those who want to enhance their understanding of both deep learning concepts and C++ programming.
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Clone the Repository:
git clone https://github.com/your-username/Deep-Paquito.git
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Build the Project:
cd Deep-Paquito make -
Run Sample Applications: Edit paquito.cpp(main) and make the network using the already provided modules, then execute and include the datasets.
If you find any issues, have suggestions, or would like to contribute, feel free to open an issue or submit a pull request.
This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.
Special thanks to kripxera1 for all the help provided with this project.