{"id":22116152,"url":"https://github.com/blacksujit/neural_network_using_numpy_and_math","last_synced_at":"2025-08-26T02:10:53.313Z","repository":{"id":249567476,"uuid":"831864435","full_name":"Blacksujit/Neural_Network_using_Numpy_and_Math","owner":"Blacksujit","description":"Inspired from a repo . i have implemented an some ground level maths and logic . In this neural network   no tensorflow , pytorch is used it is just prepared by using numpy and some logical math.","archived":false,"fork":false,"pushed_at":"2024-08-31T10:52:48.000Z","size":33,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-24T05:34:52.752Z","etag":null,"topics":["algorithmic-composition","basics-of-python","calculus","criticalthinking","innovation","machine-learning","maths","neural-network","neuralnetworkusingnumpy","numpy","purebasics"],"latest_commit_sha":null,"homepage":"https://neural-network-using-numpy-and-math.onrender.com/","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Blacksujit.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-07-21T20:46:24.000Z","updated_at":"2024-08-31T10:52:51.000Z","dependencies_parsed_at":"2024-07-21T22:29:05.003Z","dependency_job_id":"f3fd3894-f13c-4b8a-b2b7-2cf5e62ae0e8","html_url":"https://github.com/Blacksujit/Neural_Network_using_Numpy_and_Math","commit_stats":null,"previous_names":["blacksujit/neural_network_using_numpy_and_math"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Blacksujit/Neural_Network_using_Numpy_and_Math","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blacksujit%2FNeural_Network_using_Numpy_and_Math","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blacksujit%2FNeural_Network_using_Numpy_and_Math/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blacksujit%2FNeural_Network_using_Numpy_and_Math/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blacksujit%2FNeural_Network_using_Numpy_and_Math/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Blacksujit","download_url":"https://codeload.github.com/Blacksujit/Neural_Network_using_Numpy_and_Math/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blacksujit%2FNeural_Network_using_Numpy_and_Math/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":272160241,"owners_count":24883779,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-26T02:00:07.904Z","response_time":60,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["algorithmic-composition","basics-of-python","calculus","criticalthinking","innovation","machine-learning","maths","neural-network","neuralnetworkusingnumpy","numpy","purebasics"],"created_at":"2024-12-01T12:19:37.095Z","updated_at":"2025-08-26T02:10:53.264Z","avatar_url":"https://github.com/Blacksujit.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Neural Network XOR Predictor\n\n# Interesting part about this neural network:\n\n## facinated by an repo and thaught how can be a neural network be prepared from scratch from just using numpy and maths no tensorflow , no pytorch just pure basics\n\nWelcome to the Neural Network XOR Predictor! 🎉 This project demonstrates a simple neural network that learns and predicts the XOR logic gate. It showcases the power of neural networks and provides an interactive web interface to explore neural network predictions.\n\n### Live Project:\n\n[Live Demo](https://neural-network-using-numpy-and-math.onrender.com/)\n\n# Web Page:\n\n\n![image](https://github.com/user-attachments/assets/cf50e557-0011-466f-9207-a5d00b770f17)\n\n\n![image](https://github.com/user-attachments/assets/dc43980a-10a7-4f56-9c55-a99948e1dda5)\n\n\n![image](https://github.com/user-attachments/assets/4c432831-c623-4f29-8ecb-a1d9e615d519)\n\n\n\n## 📋 Project Structure\n\n```\n\nneural-network-xor-predictor_using_numpy/\n│\n├── app.py # Main Flask application file\n├── neural_network.py # Neural network implementation using NumPy\n├── templates/\n│ └── index.html # HTML file for the web interface\n├── static/\n│ └── style.css # CSS file for styling the web interface\n└── README.md # This README file\n\n```\n\n\n\n## 🛠️ Features\n\n- **Neural Network Training**: Trains a neural network to predict the XOR logic gate using Python and NumPy.\n- **Interactive Web Interface**: Built with Flask and HTML/CSS to input values and get predictions in real-time.\n- **Visualization**: Includes a Plotly-based animation to visualize the neural network layers.\n- **Educational Video**: Embedded YouTube video explaining how neural networks work.\n\n# Neural Network XOR Predictor\n\nWelcome to the Neural Network XOR Predictor! 🎉 This project demonstrates a simple neural network that learns and predicts the XOR logic gate. It showcases the power of neural networks and provides an interactive web interface to explore neural network predictions.\n\n\n## 🧑‍💻 Installation\n\n1. **Clone the repository:**\n    ```bash\n    git clone  https://github.com/Blacksujit/Neural_Network_using_Numpy_and_Math.git\n    ```\n\n2. **Set up a virtual environment:**\n    ```bash\n    python -m venv venv\n    source venv/bin/activate  # On Windows, use `venv\\Scripts\\activate`\n    ```\n\n3. **Install dependencies:**\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n4. **Run the application:**\n    ```bash\n    python app.py\n    ```\n\n5. **Navigate to `http://127.0.0.1:5000/` in your web browser.**\n\n## 🎨 Visualization\n\nThe neural network layers are visualized using Plotly. An interactive animation demonstrates how the different layers of the neural network are structured.\n\n## 🎥 Educational Video\n\nLearn how neural networks work with our embedded YouTube video:\n[How a Neural Network Works](https://www.youtube.com/embed/aircAruvnKk)\n\n## 📝 How to Contribute\n\nFeel free to fork the repository and submit pull requests. For major changes, please open an issue first to discuss what you would like to change.\n\n## 💬 Contact\n\nFor any questions or suggestions, please contact me at [nirmalsujit861@gmail.com](mailto:nirmalsujit861@gmail.com).\n\n## 🔗 Links\n\n- [Project Repository](https://github.com/Blacksujit/Neural_Network_using_Numpy_and_Math.git)\n- [Live Demo](https://neural-network-using-numpy-and-math.onrender.com/)\n\n## 📜 License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n---\n\nThank you for checking out the Neural Network XOR Predictor! We hope you find it insightful and useful. 🚀\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblacksujit%2Fneural_network_using_numpy_and_math","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fblacksujit%2Fneural_network_using_numpy_and_math","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblacksujit%2Fneural_network_using_numpy_and_math/lists"}