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https://github.com/iam-baivab/ai-development
This repository is a curated collection of resources, code, and projects focused on artificial intelligence (AI) development. Dive into deep learning, machine learning, computer vision, and Python development with our comprehensive set of tools
https://github.com/iam-baivab/ai-development
classification cross-entropy deep-learning machine-learning matplotlib opencv python regression scipy supervised-learning unsupervised-learning
Last synced: about 1 month ago
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This repository is a curated collection of resources, code, and projects focused on artificial intelligence (AI) development. Dive into deep learning, machine learning, computer vision, and Python development with our comprehensive set of tools
- Host: GitHub
- URL: https://github.com/iam-baivab/ai-development
- Owner: iam-baivab
- Created: 2024-05-31T19:52:27.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-06-01T09:10:50.000Z (6 months ago)
- Last Synced: 2024-10-15T17:29:37.645Z (about 1 month ago)
- Topics: classification, cross-entropy, deep-learning, machine-learning, matplotlib, opencv, python, regression, scipy, supervised-learning, unsupervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 39.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI Development
[![MIT License][license-shield]][license-url]
[![LinkedIn][linkedin-shield]][linkedin-url]
## Description
This project focuses on the development and application of various AI technologies. It includes deep learning neural networks (DL-NN), machine learning (ML), computer vision using OpenCV, and hands-on practice with Python in Jupyter Notebooks. The goal is to provide a comprehensive learning and development environment for AI enthusiasts and professionals.
## Table of Contents
1. [Installation](#installation)
2. [Usage](#usage)
3. [Features](#features)
4. [Practice Set](#practice-set)
5. [Documentation](#documentation)
6. [Contributing](#contributing)
7. [License](#license)
## Installation
To get started with this project, follow these steps:
1. **Clone the repository:**
```sh
git clone https://github.com/iam-baivab/AI-Development.git
cd aidevelopment
```
2. **Create a virtual environment:**
```sh
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Set up Jupyter Notebook:**
```sh
jupyter notebook
```
## Usage
### Running the Project
1. **[Deep Learning Neural Networks (DL-NN)](https://github.com/iam-baivab/AI-Development/tree/main/DL-NN):**
- This `DL-NN` folder is dedicated to deep learning and neural network projects. It includes code, models, and datasets related to training and deploying deep learning models using frameworks.
2. **[Machine Learning (ML)](https://github.com/iam-baivab/AI-Development/tree/main/ML):**
- The `ML` folder focuses on traditional machine learning algorithms and techniques. It contains code examples, notebooks, and datasets for tasks such as classification, regression, clustering, and dimensionality reduction.
3. **[OpenCV](https://github.com/iam-baivab/AI-Development/tree/main/OpenCV):**
- In the `OpenCV` folder, you will find resources for computer vision projects. This includes image and video processing, object detection and tracking, feature extraction, and other computer vision tasks using the OpenCV library.
4. **[Python](https://github.com/iam-baivab/AI-Development/tree/main/Python):**
- The `Python` folder contains general-purpose Python scripts and utilities that are used across different AI development projects. It includes helper functions, data preprocessing scripts, and other tools to streamline AI development workflows.
## Features
- **Deep Learning Neural Networks (DL-NN):** Implementation and training of various neural network architectures.
- **Machine Learning (ML):** Hands-on with regression, classification, clustering, and more.
- **OpenCV:** Practical projects on image processing and computer vision.
- **Practice Set:** A collection of Jupyter Notebooks designed for practicing Python and AI problems.
## Practice Set
The practice set includes notebooks that cover:
- Basic Python programming
- Data manipulation with pandas
- Visualization with matplotlib and seaborn
- Machine learning algorithms and their implementations
- Deep learning exercises using TensorFlow and Keras
## Documentation
Comprehensive documentation for each module is provided in their respective directories. Detailed explanations, code comments, and usage instructions are included to help users understand and extend the project.
## Contributing
Contributions are welcome! Please follow these steps to contribute:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make your changes.
4. Commit your changes (`git commit -m 'Add some feature'`).
5. Push to the branch (`git push origin feature-branch`).
6. Open a pull request.
For detailed contribution guidelines, refer to the `CONTRIBUTING.md` file.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more information.
---
[license-shield]: https://img.shields.io/badge/License-MIT-red.svg
[license-url]: https://github.com/iam-baivab/News-Scraping-using-BeautyfulSoup-Selenium-with-Django/blob/main/LICENSE
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat&logo=linkedin&colorB=blue
[linkedin-url]: https://www.linkedin.com/in/baivabsarkar/